No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Elephant Herding Optimization
Abstract
In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up. These two behaviors can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants in each clan are updated by its current position and matriarch through clan updating operator. It is followed by the implementation of the separating operator which can enhance the population diversity at the later search phase. To demonstrate its effectiveness, EHO is benchmarked by fifteen test cases comparing with BBO, DE and GA. The results show that EHO can find the better values on most benchmark problems than those three metaheuristic algorithms.
... PPSO-DA uses the implementation framework of PPSO [99] and uses the concept of distraction inspired from DA [100] PPSO and DA), comprehensive learning particle swarm optimization (CLPSO) [98], memory-based hybrid dragonfly algorithm (MHDA) [118], sine cosine algorithm (SCA) [119], and elephant herding optimization (EHO) [120] in balancing load across active PMs and among their considered resource capacities (i.e., CPU and RAM). ...
... Experiments are performed using CloudSim simulator [104] taken as 0.5 and 0.1, respectively, as given in [120]. ...
... The values of control parameters for PPSO, DA, CLPSO, MHDA, SCA, and EHO are adopted from the papers [99], [100], [98], [118], [119] and [120], respectively. The mean of RCi metric corresponding to different scheduling cycles in a simulation run is called mean resource capacity imbalance (mean RCi). ...
... It is in this family we find the vast plethora of metaheuristic techniques such as particle swarm optimisation (PSO) (Kennedy & Eberhart, 1995), genetic algorithms (GA) (Goldberg, 1989), differential evolution (DE) (Storn & Price, 1997), simulated annealing (SA) (Kirkpatrick et al., 1983), Ant colony optimisation (Dorigo & Di Caro, 1999) and a growing number of novel metaheuristics: Ant lion optimisation (Mirjalili, 2015), Jaya optimisation (JA) (Rao, 2016), Salp Swarm optimisation (Mirjalili et al., 2017), Grey wolf optimisation (Mirjalili et al., 2014), whale optimisation (Mirjalili & Lewis, 2016), Elephant herding optimisation (Wang et al., 2015), Monarch Butterfly optimisation (Wang et al., 2019). ...
... New generation metaheuristics are since then developed and growing in popularity such as Artificial bee colony (Karaboga et al., 2005), Imperialist algorithm (Atashpaz-Gargari & Lucas, 2007), Glowworm algorithm (Krishnanand & Ghose, 2005), Bacterial foraging optimisation (Passino, 2002), Bat algorithm (Yang, 2010), Intelligent water drops (Shah-Hosseini, 2009), Biogeography-based optimisation (Simon, 2008), Cuckoo search (Yang & Deb, 2009), Intelligent water drops (Hosseini, 2007), Firefly algorithm (Yang, 2009), Paddy field algorithm (Premaratne et al., 2009), Gravitational search algorithm (Rashedi et al., 2009), Grey wolf algorithm (Mirjalili et al., 2014), Harmony search algorithm (Geem et al., 2001), Whale optimisation (Mirjalili & Lewis, 2016), Krill herd (Gandomi & Alavi, 2012), Elephant herding optimisation (Wang et al., 2015), Jaya optimisation (Rao, 2016), Monarch butterfly optimisation (Wang et al., 2019), Salp Swarm optimisation (Mirjalili et al., 2017), Ant lion optimisation (Mirjalili, 2015) and others (Dokeroglu et al., 2019). The next section will discuss in greater detail the classical metaheuristics of interest to this study: particle swarm optimisation, differential evolution and genetic algorithm. ...
... The Elephant herd optimisation is a swarm intelligence inspired by the social behaviour of elephant herds (Wang et al., 2015). ...
This study investigates the issue of solution accuracy and convergence speed of continuous optimisation solvers for nonconvex multimodal functions, including their applications in control systems. The two families of global optimisation algorithms, stochastic and deterministic, are explored, focusing on tackling diversity issues on the former and slow convergence on the latter.
In the family of stochastic methods, enhancement strategies on niching algorithms have been proposed due to their ability to discover multiple global optima concurrently, thus impacting diversity and, consequently, solution accuracy.
The study concentrated on three popular classical metaheuristics: particle swarm optimisation, differential evolution and genetic algorithms in this order of interest. In the family of deterministic methods, the convergence speed of the $\alpha$BB algorithm, a complete search algorithm for twice differentiable functions, is investigated with a particular focus on the exploration capacity of its upper bound solver, which conventionally uses local search algorithms. The hybridisation of the algorithm with particle swarm optimisation and interval analysis was considered to boost its exploration capacity, tighten its bounds and thus accelerate solution search and verification time.
This investigation resulted in three novel algorithms and two control applications. A parallel approach to sequential niching is proposed with improved solution accuracy and computational efficiency for practical continuous global optimisation. The framework is used for Fractional order PID tuning and nonlinear optimal control yielding improved solution accuracy and robustness. A topologically informed niching particle swarm optimisation is proposed that improves the ratio of discovering multiple global optima in a multimodal space, an improvement over conventional cluster-based multiswarm algorithms in the literature. A Hybrid PSO-$\alpha$BB global optimisation is proposed with enhanced convergence speed of the $\alpha$BB algorithm in the complete search configuration and improved solution accuracy of particle swarm optimisation in the heuristic search configuration. The final chapter of the thesis concludes the study and proposes research directions to further ameliorate solution accuracy and convergence speed in continuous global optimisation.
Keywords: Niching algorithms, particle swarm optimisation, differential evolution, genetic algorithm, branch and bound framework, nonconvex global optimisation, optimal control, controller tuning.
... The goal of this research work is to develop a new method based on the use of the elephant herding optimization algorithm (EHO) for the search for an optimal trajectory for a mobile robot in different locations. EHO is a recent algorithm belonging to the family of swarm intelligence algorithms, proposed by [27]. Its principle is based on the simulation of the behavior of elephant herding when solving optimization problems. ...
... Elephant herding optimization (EHO), is an algorithm that belongs to the nature-inspired optimization algorithms. It is one of the most recent swarm intelligence algorithms proposed by Wang et al. [27], and its application lies in optimization problems. Even though it is a fairly recent algorithm, EHO has already been successfully applied to various numerical problems [29]- [31]. ...
Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.
... As shown below, the proposed EHO (Elephant herding optimisation) technique (Wang, Deb, & Coelho, 2015) is a novel metaheuristic nature-inspired optimisation method that discovers the ideal solution to advance multicast routing: ...
... BESS begins charging after peak hours and eventually reaches its initial capacity limit. This findings was in agreement with the work of Wang, Deb, and Coelho (2015) who posit that the nonlinear attributes of a Battery Energy Storage System (BESS) might lead to discrepancies in the stored energy between the planned operation and the actual operation. The energy held in a Battery Energy Storage System (BESS) exhibits a nonlinear relationship with the charging/discharging power. ...
Different energy sources are typically incorporated into coordinated MGS
(Micro Grid Systems) using energy management systems. It is challenging to
integrate acceptable energy management models in MGS mainly due to the
unpredictable nature, availability estimations and complexities in regulating RES
(Renewable Energy Sources). Energy policies are encouraging incorporation of RES
while reducing the usage of fossil-based fuels resulting in the need to optimize RES.
This study's major goal is to lower running costs of grid-connected MGSs while
predicting PV (photovoltaic) based electricity and load demands in near future. In
order to enhance the performance of micro-grids, this work focuses on creating a
technique for integrating optimized ANN (artificial neutral networks) into an EMS
(Energy Management System). The schema called EMS-HANN (Energy Management
System - Hybrid ANN) is proposed in this work and it includes forecasts, planning,
data gathering, and HMI (human-machine interfaces) components. Day-ahead PV
power and load demand estimates are combined with a 3-level SWT (stationary
wavelet transforms) as part of the forecasting module's enhanced hybrid forecasting
technique and GWO-HANN (grey wolf optimization-based Hybrid Artificial Neural
Network). The scheduling module employs AEHO (Adaptive Elephant Herding
Optimisation)-based scheduling to deliver the optimal power flow for grid-connected MGS. Subsequently, DAQ and HMI modules monitor, analyse, and change forecast and schedule input variables. The proposed model for applications of MGS is implemented
along with current algorithms in MATLAB/Simulink platform where outcomes
demonstrate better performances of the suggested model as compared to
comparable efforts.
... Firstly, a Logistic Chaotic mapping [13] [14] is utilized to replace the random initialization in MGO to improve the quality of the initial population. Secondly, an operator is modified to maintain diversity in the population during the execution process to avoid suboptimal solutions [15]. Thirdly, a Truncation Selection Technique [16] is adopted to determine the value of the newly introduce parameter to control the convergence speed. ...
... Finally, the update operator at the migrating to search for food (MSF) phase is modified by changing it to enhance its ability to search thoroughly within the search space for the continuous maintenance of good divergence in the population for high dimensional problems. The adopted approach has been applied in [5,15]. It is given in equation Calculate TSM, MH, BMH, and MSF using the modified eq (12), eq(6), eq (7), and eq (13) ...
In this study, a Modified Mountain Gazelle Optimizer (MMGO) algorithm is presented. The proposed algorithm was designed to improve the ability of MGO in solving high-dimensional problems, increase convergence speed, and enhance stability. The modification is based on the application of a logistics chaotic mapping at the initialization stage, a modified Migration pattern in the Search of Food (MSF) phase for diversity maintenance, and a controlling factor at the Territorial and Solitary Males (TSM) phase using the truncation selection technique. The proposed algorithm was implemented in MATLAB software and its performance was tested on 23 benchmark functions, and a real-life engineering problem to prove its efficiency and adaptability. The results of the MMGO were compared with those of the basic Mountain Gazelle Optimizer (MGO), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA). The findings of the work indicated that the MMGO outperformed the other state-of-the-art algorithms in terms of both optimization accuracy and computational efficiency. The results demonstrated the effectiveness and robustness of the proposed MMGO algorithm, in solving high-dimensional optimization problems in engineering and other fields.
... Here, four kinds of grey wolves, like alpha, beta, delta, and omega, are adapted to simulate the leadership hierarchy. Moreover, an advanced swarm-based metaheuristic search method, named Elephant Herding Optimization (EHO) [21] is devised to solve the optimization tasks. Here, the EHO is motivated from the elephant's herding behavior. ...
... Then, the removed tasks will be added in other VMs by optimally finding the VMs for the task execution. The optimal finding of VMS for executing of the removed task will be found out using the proposed EHGWO, which will be designed by combining Elephant Herding Optimization (EHO) [21] and Grey Wolf Optimizer (GWO) [20]. Figure 1 depicts the proposed EHGWO based load balancing method in Cloud computing platform. ...
The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.
... On the other hand, the elephant herd optimization (EHO) algorithm has a strong potential of achieving a global optimal solution, with robustness and fast convergence speed; it is a newly proposed intelligent algorithm (see [51] and [52]). It has proven its ability to achieve the global optimal solution by implementing it in various standard test functions [53]. ...
... In 2015, Wang et al. [51] created the EHO algorithm, taking inspiration from the social behaviors of elephant herds observed in nature. Even though elephants demonstrate intelligent behavior in real life, the EHO algorithm was created using the following idealized rules. ...
The rapid growth in greenhouse gases (GHGs), the lack of electricity production, and an ever-increasing demand for electrical energy requires an optimal reduction in coal-fired thermal generating units (CFTGU) with the aim of minimizing fuel costs and emissions. Previous approaches have been unable to deal with such problems due to the non-convexity of realistic scenarios and confined optimum convergence. Instead, meta-heuristic techniques have gained more attention in order to deal with such constrained static/dynamic economic emission load dispatch (ELD/DEELD) problems, due to their flexibility and derivative-free structures. Hence, in this work, the elephant herd optimization (EHO) technique is proposed in order to solve constrained non-convex static and dynamic ELD problems in the power system. The proposed EHO algorithm is a nature-inspired technique that utilizes a new separation method and elitism strategy in order to retain the diversity of the population and to ensure that the fittest individuals are retained in the next generation. The current approach can be implemented to minimize both the fuel and emission cost functions of the CFTGUs subject to power balance constraints, active power generation limits, and ramp rate limits in the system. Three test systems involving 6, 10, and 40 units were utilized to demonstrate the effectiveness and practical feasibility of the proposed algorithm. Numerical results indicate that the proposed EHO algorithm exhibits better performance in most of the test cases as compared to recent existing algorithms when applied to the static and dynamic ELD issue, demonstrating its superiority and practicability.
... Metaheuristic optimization algorithms have received wide attention largely as a result of their simplicity and flexibility. The following are a few of the most popular metaheuristic algorithms: Simulated Annealing (SA) [1], Bacterial Foraging Optimization algorithm (BFO) [2], Particle Swarm Optimization (PSO) [3], Moth Flame Optimization (MFO) [4], Chimp Optimization Algorithm (ChOA) [5], Whale Optimization Algorithm (WOA) [6], Artificial Bee Colony (ABC) [7], Ant Colony Optimization (ACO) [8], Elephant Herding Optimization (EHO) [9], Harris Hawks Optimization (HHO) [10], Differential Evolution (DE) [11], Crow Search Algorithm (CSA) [12], Grasshopper Optimization Algorithm (GOA) [13], Honey Badger Algorithm (HBA) [14] and Artificial Electric Field Algorithm (AEFA) [15]. There is currently a large use of metaheuristic optimization algorithms in Table 1 Meta-heuristic optimization algorithms for antenna problems. ...
This work proposed a new method to optimize the antenna S-parameter using a Golden Sine mechanism-based Honey Badger Algorithm that employs Tent chaos (GST-HBA). The Honey Badger Algorithm (HBA) is a promising optimization method that similar to other metaheuristic algorithms, is prone to premature convergence and lacks diversity in the population. The Honey Badger Algorithm is inspired by the behavior of honey badgers who use their sense of smell and honeyguide birds to move toward the honeycomb. Our proposed approach aims to improve the performance of HBA and enhance the accuracy of the optimization process for antenna S-parameter optimization. The approach we propose in this study leverages the strengths of both tent chaos and the golden sine mechanism to achieve fast convergence, population diversity, and a good tradeoff between exploitation and exploration. We begin by testing our approach on 20 standard benchmark functions, and then we apply it to a test suite of 8 S-parameter functions. We perform tests comparing the outcomes to those of other optimization algorithms, the result shows that the suggested algorithm is superior.
... Elephant Herding Optimization (EHO) is a meta-heuristic algorithm introduced by Wang et al. [28], inspired by the behaviour of elephants in the African savanna. It has demonstrated effectiveness in solving optimization problems and has been successfully applied in various domains, including feature selection. ...
Diabetes mellitus is a chronic disease that affects millions of people worldwide. Article focuses on detecting the presence of diabetes in patients using microarray gene data obtained from the pancreas. Handle the high-dimensional nature of the data, four different dimensionality reduction techniques, namely Bessel Function, Discrete Cosine Transform (DCT), Least Square Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. After reduced the data, Meta-heuristic algorithms are applied like Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Ten classification techniques are using to classify the data in both the format like without feature selection method and with feature selection method. The classification techniques are Non-Linear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), Support Vector Machine (SVM) with linear kernel, Support Vector Machine (SVM) with polynomial kernel, and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Results showed that the AAA with SVM(RBF) achieved an accuracy of 90% without feature selection. However, when feature selection was applied, with EHO of AAA with SVM(RBF) exhibited the highest accuracy of 95.714%, followed closely by with DOA of AAA with SVM (RBF) at 94.28%.
... The elephants of different clans live under the management of matriarchy. It is an algorithm developed by modelling the male elephant's feed and shelter pursuit process by leaving its family group when it becomes a grown-up [95]. It has been observed that the proposed EHObased approach for ODGA has been tested on different DN and gives the best results [56]. ...
Distributed generation (DG) has a key role in enlarging the implementation of renewable energy resources (RES). However, the intermittent and uncontrollable nature of RES can lead to several severe power quality‐related issues. Therefore, many efforts have been made to overcome these issues by optimizing DG sizes and locations. Hence, optimal DG allocation (ODGA) is significant for DG performance and provides advantages to the power system, such as improved power quality, voltage stability, reliability, and profitability. This study reviews recent ODGA studies eliminating the main DG integration problems. Often used ODGA methods have been categorized, and the main differences have been discussed, giving the details of features of optimization methods such as convergence performance and computational burden. A deep analysis for categorizing the objectives of ODGA has been done. In addition, optimization methods applied in ODGA studies have been presented by comparing the superiorities of algorithms and validated test network models. The objectives and significant findings of the ODGA applications are summarized with the advantages and disadvantages. It can be concluded that ODGA has a critical role in RES integration on the DG side and in reducing carbon emissions. This paper leads and provides a perspective for researchers working on recent ODGA methods.
... There are some algorithms based on elephants, such as elephant herding optimization (EHO), which was developed based on the herding behavior of elephant groups [227]. The elephant search algorithm (ESA) is based on similar concepts [228]. ...
This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.
... Elephant Herding Optimization (EHO) [61] is a technique based on swarm intelligence and inspired by the social behavior that occurs in herds of elephants. Elephants are social animals with complex structures comprising females and young. ...
The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient. This computation allows invariance in the global brightness changes in the images, but it is computationally more expensive when using images of larger dimensions, or even sets of images. Furthermore, we investigate the performance of six different swarm intelligence techniques aiming to accelerate the target search process. To evaluate the proposed design, the processing time, the number of iterations, and the success rate were compared. The results show that it is possible to obtain approaches capable of processing video images at 30 frames per second with an acceptable average success rate for detecting the tracked target. The search strategies based on PSO, ABC, FFA, and CS are able to meet the processing time of 30 frame/s, yielding average accuracy rates above 80% for the pipelined co-design implementation. However, FWA, EHO, and BFOA could not achieve the required timing restriction, and they achieved an acceptance rate around 60%. Among all the investigated search strategies, the PSO provides the best performance, yielding an average processing time of 16.22 ms coupled with a 95% success rate.
... In recent times, a hybrid combination of algorithms is gaining significance because of its superiority over basic algorithms. In this article, a novel hybrid mix of the Jaya algorithm (JA) [15,16] and the enhanced elephant herding algorithm (EEHA) [17,18] is proposed to solve the allotment problem of SPVDG and BESS. This hybrid technology has the potential to overcome both the algorithms' shortcomings and improve the performance to get the best global solution. ...
The adoption of renewable energy sources (RES) such as solar photovoltaic distributed generation (SPVDG) has been at its peak in the present decade owing to their positive impact on the environment and the grid. However, these sources' ratings and placement need to be optimally estimated before their accommodation in the power distribution system (PDS) to prevent increased power loss and voltage profile aberrations. In this article, SPVDG is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG, such as intermittency and uncertainty, and to reduce peak demand. This paper presented a novel hybrid algorithm, which is a combination of the enhanced elephant herding algorithm and the Jaya algorithm. This developed algorithm properly controls the searching algorithm from global exploration to local exploitation to obtain the near-global optimum solution. The problem formulation is centered on the optimal accommodation of SPVDG and BESS to alleviate the power loss and improve the voltage profile of the PDS. Further, the voltage limits, maximum current limits, and BESS charge-discharge constraints are validated throughout the optimization process. Moreover, the hourly variation of SPVDG generation and demand profile with seasonal impact is examined in this study. IEEE 69 bus PDS is tested for the development of the presented work. The suggested algorithm demonstrated its effectiveness and accuracy when compared to different optimization approaches in the literature.
... The EHA is a heuristic scheme developed by mimicking the food-foraging behavior of an elephant herd, which is guided by a group leader (matriarch). The essential information for the EHA can be found in [45]. The theory behind the EHA includes the following conditions: Each clan contains a fixed number of members (elephants) in the herd, and the adult male elephants leave the herd and live alone during each generation. ...
Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.
... ACO mathematically modeled the ants' search for the shortest path to reach the food source. Grey Wolf Optimizer [20], Tunicate Swarm Algorithm [21], Grasshopper Optimization Algorithm [22], Elephant Herding Optimization [23], Manta Ray Foraging Optimization [24], Artificial Bee Colony [25], Firefly Algorithm [26], Butterfly Optimization Algorithm [27], and Krill Herd Algorithm [28] are the other algorithms based on swarm intelligence. ...
Taraklı district of Sakarya province, which constitutes qualified examples of West Black Sea architecture, is an Ottoman settlement that has preserved its original texture by being shaped in organic texture with the effect of climatic and topographic features. The fact that it is located in the region where forest areas are dense has revealed the purpose of use of wood material. Wood is among the most preferred building materials due to it is light, easy to process and transportable. At the same time, its compatibility with nature, ease of recycling, high strength and long service life have an important place in traditional Turkish architecture.
In this study, the construction techniques and materials used in the traditional architecture of Taraklı were examined. The load bearing systems applied on wooden building walls have been classified according to the way they work under the influence of load. The load bearing systems applied on wooden building walls are classified according to the way working under the influence of load. By providing informations about foundation walls as vertical carriers
and timber framed walls, structure cover systems and structure elements have been
investigated and supported in which cases caused by deterioration was investigated and supported with photographs. The wood material used in traditional civil architecture examples forms the backbone of the structure in wooden structures. For this reason, it has been revealed that the woods of resistance to vertical and horizontal loads in the building, it is not deformation and long standing depend on the qualities that determine the value of the structure. In terms of the preservation and sustainability of the original qualities of traditional architecture, it is necessary to know the properties of the wood material to be used in the structures and to design it well.
... In this way, the observed problem would move to the domain of multicriteria optimization which is another possible future research direction. To solve a multi-criteria optimization model structured like this, in cases where there are a large number of existing locations of service network facilities and user nodes, it is necessary to apply one of the many existing metaheuristics such as the colony predation algorithm (Tu et al., 2021), earthworm optimization algorithm , elephant herding optimization (Wang et al., 2015), hunger games search (Yang et al., 2021), monarch butterfly optimization (Wang et al., 2019), slime mould algorithm (Li et al., 2020), etc. In this sense, future research will go in the direction of solving problems of larger dimensions, which would be a service network of realistic dimensions. ...
The number and locations of facilities represent the most important decisions when modeling service networks. The facility location problem in the context of service networks is predetermined by the investment costs and/or achieving a certain standard of satisfying users' demand. Systems designed in this way are based on the idea that they will function in regular exploitation conditions, without any interference. However, various adverse events caused by intent, unintentional human activities, technological disasters or natural disasters can lead to a partial or complete cessation of the service networks. For the first time, this paper highlights the importance of the impact assessment of disruption events on the service networks where the r-interdiction median location model is presented as a potential solution approach in a case when these events occur. Also, an extensive overview of the state-of-the-art literature is provided. Finally, a numerical example of the determination of the most vulnerable points of service networks is given to illustrate the effects of potential disruptions, as well as appropriate preventive actions that eliminate or at least mitigate those situations.
... The main solutions for many applications are optimization techniques for the best tuning of PI control settings. For the best tuning of PI control parameters, one contemporary optimization approach is called EHO. 45,46 In this article, EHO is used to lessen the objective function J, that is can be defined as: ...
Despite having higher maintenance costs than AC motors, DC motors had been widely employed in the industry due to their outstanding speed control capabilities. This employment increased due to the DC output of some renewable sources recently. This article introduces the speed control of DC motors using model reference adaptive control (MRAC). This control is achieved through regulating the armature voltage at different load changes. A comparison between the proposed adaptive controller and optimized PI controller using the elephant herding optimization (EHO) is presented. The PI controller parameters were optimality adjusted to minimize the integral absolute error, minimum overshoot, and minimum settling time. Computer simulations show that the suggested MRAC is preferable to a traditional optimized PI controller. In addition, the proposed controller is effective in regulating the DC motor over a broad range of operating speeds.
Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: “Proceedings of the Congress” (33, 10.09%), “Applied Soft Computing” (19, 5.81%), “Expert Systems with Applications” (18, 5.51%), “Knowledge-Based Systems” (12, 3.67%), “Engineering Applications of Artificial Intelligence” (12, 3.67%), “Advances in Engineering Software” (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece.
Activity-based scheduling optimization is a combinatorial problem built on the traveling salesman problem intending to optimize people schedules considering their trips and the available transportation network. Due to the difficulty of scheduling, traditional and exact methods are unable to provide appropriate solutions. Hence, new approaches have been introduced in the literature to settle these complex problems. One group of new techniques is known as metaheuristic algorithms, which provides a robust family of problem-solving methods created by mimicking natural phenomena. Although these new techniques might not find an optimal solution, they can find a near-optimal one in a moderate period. Furthermore, a myriad of novel algorithms has been introduced making it tedious for academics to select the appropriate technique. Thus, this paper investigates the contribution of metaheuristics to solve transportation-related optimization problems. To achieve this aim, we conducted a bibliometric analysis, and defined the descriptive and assessment features for 120 metaheuristics. The findings of the study reveal the usage tendencies of the algorithms, identify the most prevalent ones, and highlight those metaheuristics that have a potential use in upcoming research. The results demonstrate that the most applied metaheuristic algorithm is the genetic algorithm, but the ant colony optimization algorithm is the most popular one based on the number of citations. Lastly, we open a discussion on a few unexplored research gaps and expectations.
Churn prevention has always been a top priority in business retention. The significant problem of customer churn was confronted by the telecommunications industry due to saturated markets, harsh competition, dynamic criteria, as well as the launch of new tempting offers. By formalizing the telecom industry’s problem of churn prediction as a classification task, this work makes a contribution to the field. To effectively track customer churn, a churn prediction (CP) model is needed. Therefore, using the deep learning model known as the reformatted recurrent neural network in conjunction with the Elephant herding optimization (EHO) method, this work provides a novel framework to forecast customer turnover (R-RNN). EHO is a meta-heuristic optimization algorithm that draws inspiration from nature and is based on the herding behaviour of elephants. The distance between the elephants in each clan in relation to the location of a matriarch elephant is updated by EHO using a clan operator. For a wide range of benchmark issues and application domains, the EHO approach has been shown to be superior to several cutting-edge meta-heuristic methods. In order to classify the Churn Customer (CC) and a regular customer, RRNN is modified. This improved EHO effectively optimises the specific RNN parameters. If a client churns as a result, network usage is examined as a retention strategy. However, this paradigm does not take into account the number of consumers who leave based on how often they use their local networks. The results of the simulation and performance metrics-based comparison are assessed to show that the newly proposed technique can identify churn more successfully than pertinent techniques.
In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods.
At the distribution level, the traditional approach of pricing for voltage control ancillary service shows certain disadvantages as it considers only production costs. For the optimized procurement and pricing of active power and reactive power provision from reactive power compensating devices (RPCDs) such as APFCs, D-STATCOM and converter-controlled distributed generators (CCDGs), its location, availability, as well as its capability, are of utmost importance. For better pricing signals, the pricing structure for active power and reactive power compensation service needs to consider all major cost components like depreciation cost, cost of losses, capital cost, and balance of system cost. In this work, an attempt has been made to address the procurement and pricing issues at distribution level, by proposing optimized active power and reactive power management (OARPM) and developing ‘Extended Nodal Pricing Function' (‘ENPF’) for its pricing. This is specially developed for pricing the optimized active power and reactive power provision cost through RPCDs and CCDGs installed in grid-connected microgrid at distribution level achieving system performance improvement and bring economic savings. To test the impact of proposed OARPM and ENPF, five different cases are analyzed using Indian real-time distribution system data. The impact of proposed methodology is observed on 5 aspects of system performance that are dispatch of active power and reactive power, power losses minimization, objective function cost minimization, active power and reactive power DLMP and voltage control. The methodology is generic to implemented for pricing in any electricity market.
These days, an overarching dilemma in the energy performance sphere of buildings is establishing multi-criteria optimizing processes of actual house design. To weather such an issue, a novel procedure on the basis of a meta-model (MM) is introduced in this work. The EnergyPlus™ is employed as the modeling means for simulating the building performance, then a couple of the multi-criteria Improved Atom Search Optimizer (IASO) are employed which are dynamically integrated with the (ANN-MM) artificial neural network MMs. For the creation of employed validation and training samples of ANN-MMs, an optimal approach is illustrated via this technique for minimizing the entire dwelling energy modelings required for their training and confirming accurate outcomes of the optimizing. What is more, the technique is utilized for optimizing the energy efficacy and thermal comfort condition of an actual housing to obtain the optimal equilibrium among the cooling and heating features of the studied housing. Eleven design variables are chosen for this building. For more validation, the ultimate outcomes are also compared with the “true” Pareto front gained over an optimizing procedure on a simulation basis. Subsequently, seventy-seven percent of the required modeling number for gaining the Pareto set of an actual multi-criteria dwelling efficacy optimizing issue is reduced via the offered technique and accurate outcomes are obtained.
The osprey optimization algorithm (OOA) is a new metaheuristic motivated by the strategy of hunting fish in seas. In this study, the OOA is applied to solve one of the main items in a power system called economic load dispatch (ELD). The ELD has two types. The first type takes into consideration the minimization of the cost of fuel consumption, this type is called ELD. The second type takes into consideration the cost of fuel consumption and the cost of emission, this type is called combined emission and economic dispatch (CEED). The performance of the OOA is compared against several techniques to evaluate its reliability. These methods include elephant herding optimization (EHO), the rime-ice algorithm (RIME), the tunicate swarm algorithm (TSA), and the slime mould algorithm (SMA) for the same case study. Also, the OOA is compared with other techniques in the literature, such as an artificial bee colony (ABO), the sine cosine algorithm (SCA), the moth search algorithm (MSA), the chimp optimization algorithm (ChOA), and monarch butterfly optimization (MBO). Power mismatch is the main item used in the evaluation of the OOA with all of these methods. There are six cases used in this work: 6 units for the ELD problem at three different loads, and 6 units for the CEED problem at three different loads. Evaluation of the techniques was performed for 30 various runs based on measuring the standard deviation, minimum fitness function, and maximum mean values. The superiority of the OOA is achieved according to the obtained results for the ELD and CEED compared to all competitor algorithms.
Nonlinear industrial system modelling entails two critical phases: The first is selecting a method in order to estimate the parameter list values, and the second is selecting a proper model structure with a relatively short parameter list. Developing a comprehensive model for an industrial design process is critical for the model‐based control system. This article presents a model‐based strategy that aims to develop three linear and three nonlinear dynamic models using three well‐known meta‐heuristic optimization algorithms to simulate a challenging plant‐wide process. As a case study, an industrial real winding process (WP) is targeted to accomplish the aim of this study. The algorithms have been optimized to find the best weights of the inputs of the WP with a key issue to effectively describe the behaviour aspects of the process. To test the validity of the developed models, a series of experiments were carried out on each of the developed linear and nonlinear models. Several relevant evaluation metric measures are used to demonstrate the models' performance level. The experimental results for training and test sets of 1250 independent samples for each set based upon the proposed modelling schemes show that the mean square error to correctly model the WP occurred in less than 0.001. A comparison of the developed intelligent linear and nonlinear models with the Auto‐Regressive Integrated Moving Average (ARIMA) and Multiple Linear Regression (MLR) models obtained through the evaluation criteria asserts the effectiveness of the proposed models‐based approaches.
This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.
Swarm intelligence algorithms are an important study field of artificial intelligence, and received a lot of attention in the areas, such as parameter optimization, data mining, image processing and signal processing. They draw on the characteristic of social animals that can gather and share the information to make a better decision than individuals. And thus, it makes all the algorithms need an iterative process. For nearly several decades, more than 100 promising algorithms have been proposed. Are these algorithms suitable for all types of problems? How do they relate to the maximum iterations? In this study, 45 test functions from the classical set, CEC2019 and CEC2022 are classified into different problems according to their features, and 123 swarm intelligence algorithms are evaluated on a large scale with different maximum iterations. The experiment results show that most of the algorithms are suitable for low and medium dimensional problems where 5 algorithms (BES, CHOA, ESDA, FBI, and SFS) have the best optimization performance and robustness on these problems. Several algorithms are suitable for the problems with different complexities where 5 algorithms (BES, FA, MPA, SA, and SFS) have the best performance of the problems. Very few algorithms are suitable for the problems with different search space sizes where the CHIO is very robust in the problems. Besides, 3 algorithms (LSO, DE, and RSA) are the fastest.
This article introduced a new approach known as adaptive exponential particle swarm optimization (AEPSO) for the purpose of load frequency control to damp out the oscillations of frequency response as well as improve the dynamic performance of a single area non-reheat thermal power system. The control parameters of the PID controller are designed using AEPSO with an adaptive inertia weight to overcome the local optima achieving the best solution. The nonlinearity, i.e., governor dead-band (GDB), is considered to make the scheme more realistic. As the load variation is random throughout the year, the performance of the system is rigorously tested for wide load variation, i.e., from 10 to 60%. The performance of AEPSO is compared with general PSO (GPSO) for various loading conditions. Here, it has been found that the proposed technique, i.e., AEPSO endowed better performance compare to GPSO in all respect.KeywordsLFCAEPSOPID controllerAdaptive inertia weight
A large number of sensors are deployed for performing various tasks in the smart cities. The sensors are connected with each other through the Internet that leads to the emergence of Internet of Things (IoT). As the time passes, the number of deployed sensors is exponentially increasing. Not only this, the enhancement of sensors has also laid the base of automation. However, the increased number of sensors make the IoT networks more complex and scaled. Due to the increasing size and complexity, IoT networks of scale-free nature are found highly prone to attacks. In order to maintain the functionality of crucial applications, it is mandatory to increase the robustness of IoT networks. Additionally, it has been found that scale-free networks are resistant to random attacks. However, they are highly vulnerable to intentional, malicious, deliberate, targeted and cyber attacks where nodes are destroyed based on preference. Moreover, sensors of IoT network have limited communication, processing and energy resources. Hence, they cannot bear the load of computationally extensive robustness algorithms. A communication model is proposed in this paper to save the sensors from computational overhead of robustness algorithms by migrating the computational load to back-end high power processing clusters. Elephant Herding Robustness Evolution (EHRE) algorithm is proposed based on an enhanced communication model. In the proposed work, 6 phases of operations are used: initialization, sorting, clan updating, clan separating,selection and formation, and filtration. These process collectively increase the robustness of the scale-free IoT networks. EHRE is compared with well-known previous algorithms and is proven to be robust with a remarkable lead in performance. Moreover, EHRE is capable to achieve global optimum results in less number of iterations. EHRE achieves 95% efficiency after 60 iterations and 99% efficiency after 70 iterations. Moreover, EHRE performs 58.77% better than Enhanced Differential Evolution (EDE) algorithm, 65.22% better than Genetic Algorithm (GA), 86.35% better than Simulating Annealing (SA) and 94.77% better than Hill climbing Algorithm (HA).
The article presents the popularity of nature-based optimization methods in the context of issues related to digital filters. An analysis was made concerning the number of publications in popular scientific databases. Attention was paid to publications that are related to nature-inspired optimization methods and their application to digital filters design. The results obtained from scientific databases make it possible to identify the most popular optimization methods in the context of their applications to digital filters. In this paper, we also point out the thematic areas in which the articles related to nature-inspired optimization methods and digital filters are published more often.
Optimization plays a crucial role in the elevator group control system (EGCS) since various unpredictable factors, such as future traffic demand of each floor, passengers’ random destinations, and indiscriminate starting-stopping of elevators, are incorporated in scheduling a group of elevators. When solving the optimization problem of EGCS, a number of dynamic performance indices, including average waiting time, average journey time, energy consumption, etc., have to be taken into account. Until now, numerous optimization approaches have been utilized to solve the car-dispatching problem of vertical transportation. Among those methods, in this study, the authors concentrate on various metaheuristic techniques that were implemented to optimize the metrics of EGCS. While establishing a metaheuristic approach, all the previous authors recognized various factors and limitations, which ought to analyze to develop a new metaheuristic-based EGCS. Owing to this, EGCS implemented via metaheuristic techniques is summarized in this review study, together with the underlying contributions, fitness functions, computational time, and limitations. What is more, performance comparisons of different previously implemented metaheuristic approaches are depicted in this study. This research will not only assist to figure out optimal elevator group optimization algorithms, but also will shrink the technological gap by outlining a number of potential future research lines and methodologies.
An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e ., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.
The Internet of Things (IoT) brings together the real and online world to enhance services such as reducing traffic congestion, improving health care, and increasing customer service speed through quicker communication and better analysis. The manufacturing industry’s embrace of new technologies that include wireless sensor networks, embedded devices, cloud systems, and big data has enabled the emergence of Industry 4.0. The Fourth Industrial Revolution has resulted in a technologically improved globalized world in which modern technologies may directly manage the manufacturing industry’s machinery, facilities, plants, and infrastructure, as well as affect intelligent procedural and strategic decisions. The emphasis of this study is on the implementation of a novel metaheuristic optimization method. It is used for feature selection (FS), hyperparameter tuning, and training of extreme learning machines (ELMs). In terms of hyperparameter optimization (HPO), the count of neural cells in the intermediate coat of the ELM network, accompanied by the initialization of weights and biases, will be taken into account. The proposed methods have been developed for improving IoT security and were therefore tested on a Windows 10 security from the TONIot base.KeywordsSwarm intelligenceOptimizationIoTSecurityIntrusion detectionArithmetic optimization algorithmExtreme learning machine
Parameters identification of photovoltaic (PV) models is significant for forecasting power of PV system and simulating the PV models. To accurately identify the parameters of different PV models based on measured current–voltage characteristics, a novel algorithm inspired by triangle vertex and edge, called triangle search optimization (TSO), is proposed in the paper. The TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators for exploration and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the diversity of the population. The experiments on CEC 2017 test suite show that the proposed TSO performs better than the state-of-the-art algorithms in convergence accuracy. The novel algorithm, TSO, is employed to solve the parameters identification problems of single-diode PV model, double-diode PV model and PV module. Comprehensive experiments indicate that TSO can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and robustness.
A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of “Survival of the fittest ”, this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy Remora Optimization Algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.
This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the Jellyfish Swarm active passive mechanism and novel random operator in the elementary Smell Agent Optimizer (SAO). The objective of modification is to improve the global convergence
speed, exploration-exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using twenty-nine IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10-20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously
Today, an important problem of the building energy performance area is carrying out multi-criteria optimizations of real building designs. To solve this problem, a new method based on a meta-model is proposed in this study. Hence, the EnergyPlus™ is used as the simulation tool for the performance simulation of the building, then a couple of the multi-criteria Modified Coot Optimization Algorithm (MCOA) dynamically combined with the artificial neural network meta-models (ANN-MM) are employed. For the sample generation applied for training and validation of ANN meta-models, an optimum way is presented by this method to minimize the whole building energy simulations needed for their training, and validate precise results of optimization. Moreover, the method is used for the thermal comfort and energy efficiency optimization of a real house to achieve the optimum balance between the heating and cooling behavior of the case building. 12 effective design variables of this case study are selected. Also, the achieved results are put in comparison with the "true" Pareto front found through an optimization method based on simulation performed for more validation. It is assumed that 1280 points are adequate in this case study to obtain precise results on the Pareto set. Thus, 75% of the required simulations' number based on physics has been saved by this size of sample considering the 5120 applied in the method based on simulation. Consequently, the optimum Pareto set of a real multi-criteria building efficiency optimization problem is achieved by the proposed method and accurate results are achieved.
Due to the tremendous advancements in technology and development in cloud computing, it has become more common for businesses to implement virtualization in data centers to fully use their hardware resources. As a result, virtualization and security have recently undergone several modifications. Virtualization and its remarkable design provide various benefits and features over non-virtualized systems. Nonetheless, these new characters provide new weaknesses and potential attacks in virtualized systems. This paper presents a novel two-stage secure virtualization paradigm in the cloud. At first, the “Improved holoentropy and Exponential Moving Average features” are derived. The derived features are then classified using the deep belief network to identify the attacks in the network. To improve the detection accuracy, the weights of DBN will be optimally tuned using the monarch butterfly algorithm. Further, analysis is done based on varied metricsKeywordsVirtualizationOptimizationCloud computingMonarch butterfly optimizationDeep belief network
This paper explores hyperparameter optimization and training of extreme learning machines (ELM) applied to diabetes diagnostics. Early detection of diabetes is vital, as timely treatment significantly improves the quality of life of those affected. One of the toughest challenges facing artificial intelligence (AI) is the selection of control parameters suited to the problem being addressed. This work proposes a metaheuristics-based approach for adjusting the number of neurons in a single hidden layer of an artificial neural network in an ELM, as well as the selection of weights and biases (training) of every neuron in the hidden layer. Additionally, an exploration of the planet optimization algorithm’s (POA) potential for addressing NP difficult tasks is conducted. Through the process of hybridization with the firefly algorithm (FA), the POAs’ performance is further improved. The resulting algorithm is tasked with selecting optimal control parameter values for an ELM tackling diabetes diagnostics. A comparative analysis of the ELM tuned by the proposed PAO firefly search (POA-FS) metaheuristics with other state-of-the-art algorithms tasked with the same challenge strongly indicates that the suggested ELM-POA-FS displays superior performance, clearly outperforming contemporary algorithms tackling the same task that it was tested against.KeywordsExtreme learning machinePlanet optimization algorithmDiabetesOptimizationMetaheuristics
Recently, Gandomi and Alavi proposed a new heuristic search method, called krill herd (KH), for solving global optimisation problems. In order to make KH more effective, a hybrid meta-heuristic cuckoo search and krill herd (CSKH) method is proposed for function optimisation. The CSKH introduces krill updating (KU) and krill abandoning (KA) operator originated from cuckoo search (CS) during the process when the krill updating so as to greatly enhance its effectiveness and reliability dealing with numerical optimisation problems. The KU operator inspires the intensive exploitation and allows the krill individuals implement a careful search in the later run phase of the search, while KA operator is used to further enhance the exploration of the CSKH in place of a fraction of the worse krill at the end of each generation. The effectiveness of these improvements is tested by 14 standard benchmarking functions and experimental results show, in most cases, this hybrid meta-heuristic CSKH algorithm is more effective and efficient than the original KH and other approaches.
The Big Bang-Big Crunch (BB-BC) optimization algorithm is a new optimization method that relies on the Big Bang and Big Crunch theory, one of the theories of the evolution of the universe. In this paper, a discrete Big Bang-Big Crunch algorithm is presented for optimal design of structures. The BB-BC is employed to optimize different types of skeletal structures with discrete variables including trusses and frames. The results demonstrate the efficiency of the discrete BB-BC algorithm compared to other heuristic algorithms.
This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.
This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.
The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.
The differential evolution algorithm (DE) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, the proposed algorithm uses two mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, two new rules are combined through a probability rule. Then, self-adaptive parameter setting is introduced as uniformly random numbers to enhance the diversity of the population based on the relative success number of the proposed two new parameters in a previous period. In other aspects, our algorithm has a very simple structure and thus it is easy to implement. To verify the performance of MDE, 16 benchmark functions chosen from literature are employed. The results show that the proposed MDE algorithm clearly outperforms the standard differential evolution algorithm with six different parameter settings. Compared with some evolution algorithms (ODE, OXDE, SaDE, JADE, jDE, CoDE, CLPSO, CMA-ES, GL-25, AFEP, MSAEP and ENAEP) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.
Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
The applicability of peer-to-peer (p2p) in the domain of grid computing has been an important subject over the past years. Nevertheless, the sole merger between p2p and the concept of grid is not sufficient to guarantee nontrivial efficiency. Some claim that ant colony optimization (ACO) algorithms might provide a definite answer to this question. However, the use of ACO in grid networks causes several problems. The first and foremost stems out of the fact that ACO algorithms usually perform well under the conditions of static networks, solving predetermined problems in a known and bound space. The question that remains to be answered is whether the evolutive component of these algorithms is able to cope with changing conditions; and by those we mean changes both in the positive sense, such as the appearance of new resources, but also in the negative sense, such as the disappearance or failure of fragments of the network. In this paper we study these considerations in depth, bearing in mind the specificity of the peer-to-peer nature.
A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.
The cuckoo search algorithm is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, we use a new search strategy based on orthogonal learning strategy to enhance the exploitation ability of the basic cuckoo search algorithm. In order to verify the performance of our approach, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.
In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and with more certainty than manyother acclaimed global optimization methods. The newmethod requires few control variables, is robust, easyto use, and lends itself very well to parallelcomputation.
This paper presents a ranked differential evolution (RDE) algorithm for solving the identification problem of non-linear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.
In order to fully exploit global search and local search ability of krill herd (KH), a multi-stage krill herd (MSKH) algorithm is presented to solve global numerical optimization problem. The proposed method involves exploration and exploitation stage. The exploration stage uses a basic krill herd (KH) algorithm to select a good candidate solution set. It is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance its efficiency and reliability when solving global numerical optimization problems. The elitism scheme is also applied to make MSKH method move towards best solution all the time. The effectiveness of the MSKH is demonstrated by twenty-five standard and rotated and shifted benchmark problems. The results clearly demonstrate the efficiency of the proposed MSKH algorithm as compared to the standard version of the KH and other eight optimization methods.
A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.
Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.
Purpose
– Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems.
Design/methodology/approach
– The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence.
Findings
– A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO.
Originality/value
– A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.
In nature, the eastern North American monarch population is known for its southward migration
during the late summer/autumn from the northern United States and southern Canada to Mexico,
covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a
new kind of nature-inspired metaheuristic algorithm, called Monarch Butterfly Optimization
(MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals
are located in two distinct lands viz. Southern Canada & northern United States (Land 1) and
Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways.
Firstly, the offsprings are generated (position updating) by migration operator, which can be
adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by
means of butterfly adjusting operator. In order to keep the population unchanged and minimize
fitness evaluations, the sum of the newly-generated butterflies in these two ways remains equal to
the original population. In order to demonstrate the superior performance of the MBO algorithm, a
comparative study with five other metaheuristic algorithms through thirty-eight benchmark
problems, is carried out. The results clearly exhibit the capability of the MBO method towards
finding the enhanced function values on most of the benchmark problems w.r.t. the other five
algorithms.
A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH-QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH-QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH-QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH-QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.
By simulating the echolocation behavior of bats in nature, bat algorithm (BA) is proposed for global optimization that is a recently developed nature-inspired algorithm. Since then, it has been widely used in various fields. Bat algorithm balance the global search and local search by adjusting loudness and pulse rate. However, there is so many loudness and pulse rate combinations that it is hard to choose the most proper one for different problems. In this paper, a multi-swarm algorithm, called multi-swarm bat algorithm (MBA), is proposed for global search problem. In MBA method, immigration operator is used to exchange information between different swarms with different parameter settings. Thus, this configuration can make a good trade-off between global and local search. In addition, the best individuals of every swarm is put into the elite swarm through selection operator. The bat individuals in elite swarm pass over next generation without performing any operators, and this can ensure these best solutions cannot be damaged during optimization process. In order to evaluate the efficiency of MBA method, MBA has been benchmarked by sixteen standard test functions by comparing with basic BA. The results show that the MBA method is able to search more satisfactory function values on most benchmark problems than BA.
This paper proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented. The proposed algorithm is benchmarked in three phases. Firstly, a set of 19 mathematical functions is employed to test different characteristics of ALO. Secondly, three classical engineering problems (three-bar truss design, cantilever beam design, and gear train design) are solved by ALO. Finally, the shapes of two ship propellers are optimized by ALO as challenging constrained real problems. In the first two test phases, the ALO algorithm is compared with a variety of algorithms in the literature. The results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence. The ALO algorithm also finds superior optimal designs for the majority of classical engineering problems employed, showing that this algorithm has merits in solving constrained problems with diverse search spaces. The optimal shapes obtained for the ship propellers demonstrate the applicability of the proposed algorithm in solving real problems with unknown search spaces as well. Note that the source codes of the proposed ALO algorithm are publicly available at http://www.alimirjalili.com/ALO.html.
In this paper, a novel particle swarm optimisation with wavelet mutation (PSOWM) has been applied for the optimal designs of three non-uniform circular antenna arrays with optimal side lobe level (SLL) reduction. Circular array antennas laid on x-y plane are assumed. PSOWM incorporates a new definition of swarm updating with the help of wavelet theory. Wavelet mutation enhances the PSO to explore the solution space more effectively compared to the other optimisation methods. Thus, PSOWM is apparently free from getting trapped at local optima and premature convergence. The approach is illustrated through 8-, 10-, and 12-element circular antenna arrays. Various simulation results are presented and radiation pattern performances are analysed. The simulation results show PSOWM outperforms GA (Panduro et al., 2006), PSO (Sahib et al., 2008), SA (Rattan et al., 2009), and BBO (Singh and Kamal, 2011) in the optimal design of three non-uniform circular antenna arrays by achieving much greater reduction in SLL and much more improved first null beamwidth (FNBW) and 3 dB beamwidth.
The Asian elephant has had a unique cultural association with people. Unfortunately, elephants and people have also been in conflict, resulting in the decline in elephants throughout their former range in Southern Asia. This book provides an ecological analysis of elephant human interaction and its implications for the conservation of elephants. The foraging habits of elephants and their impact on vegetation are considered, along with the interactions that occur between elephants and humans. The ecological data provide the basis for recommendations on elephant conservation and management, keeping in view the socioeconomic imperatives of the Asian region.This first comprehensive account of Asian elephant ecology will be of particular interest to conservation biologists and mammalogists.
Optimization problems can become intractable when the search space undergoes tremendous growth. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods, also called metaheuristics, are the general skeletons of algorithms that can be modified and extended to suit a wide range of optimization problems. Various researchers have invented a collection of metaheuristics inspired by the movements of animals and insects (e.g., firefly, cuckoos, bats and accelerated PSO) with the advantages of efficient computation and easy implementation. This paper studies a relatively new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The WSA is tested quantitatively with different values of parameters and compared to other metaheuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms, with superior results observed in most tests.
Bat algorithm (BA) is one of the recently proposed heuristic algorithms imitating the echolocation behavior of bats to perform global optimization. The superior performance of this algorithm has been proven among the other most well-known algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). However, the original version of this algorithm is suitable for continuous problems, so it cannot be applied to binary problems directly. In this paper, a binary version of this algorithm is proposed. A comparative study with binary PSO and GA over twenty-two benchmark functions is conducted to draw a conclusion. Furthermore, Wilcoxon's rank-sum nonparametric statistical test was carried out at 5 % significance level to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The results prove that the proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions. In addition, there is a real application of the proposed method in optical engineering called optical buffer design at the end of the paper. The results of the real application also evidence the superior performance of BBA in practice.
For the purpose of enhancing the search ability of the cuckoo search (CS) algorithm, an improved robust approach, called HS/CS, is put forward to address the optimization problems. In HS/CS method, the pitch adjustment operation in harmony search (HS) that can be considered as a mutation operator is added to the process of the cuckoo updating so as to speed up convergence. Several benchmarks are applied to verify the proposed method and it is demonstrated that, in most cases, HS/CS performs better than the standard CS and other comparative methods. The parameters used in HS/CS are also investigated by various simulations.
The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.
In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.
This paper develops a methodology to obtain the optimum design of the gravity and reinforced cantilever retaining walls in terms of least-cost, having different cases of backfill satisfying the stability criteria, according to the height and properties of earth that the wall are required to support. An Enhanced Charged System Search Algorithm (ECSS) is utilized to find the economical sections as the output after minimizing the cost. The ECSS is one of the recently developed meta-heuristic algorithms that is inspired by the Coulomb and Gauss’s laws of electrostatics in physics. In order to evaluate the efficiency of this algorithm, some numerical examples are utilized. Comparing the results of the retaining wall designs obtained by the other methods illustrates a good performance of the ECSS.
Purpose
– To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues.
Design/methodology/approach
– In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability.
Findings
– This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH.
Originality/value
– Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.
Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches.
This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Lévy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods.
Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms.
The Multi-Layer Perceptron (MLP), as one of the most-widely used Neural Networks (NNs), has been applied to many practical problems. The MLP requires training on specific applications, often experiencing problems of entrapment in local minima, convergence speed, and sensitivity to initialization. This paper proposes the use of the recently developed Biogeography-Based Optimization (BBO) algorithm for training MLPs to reduce these problems. In order to investigate the efficiencies of BBO in training MLPs, five classification datasets, as well as six function approximation datasets are employed. The results are compared to five well-known heuristic algorithms, Back Propagation (BP), and Extreme Learning Machine (ELM) in terms of entrapment in local minima, result accuracy, and convergence rate. The results show that training MLPs by using BBO is significantly better than the current heuristic learning algorithms and BP. Moreover, the results show that BBO is able to provide very competitive results in comparison with ELM.
A novel robust meta-heuristic optimization algorithm, which can be considered as an improvement of the recently developed firefly algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of information exchange between the top fireflies, or the optimal solutions during the process of the light intensity updating. The detailed implementation procedure for this improved meta-heuristic method is also described. Standard benchmarking functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of this improved firefly algorithm (IFA) is superior to or at least highly competitive with the standard firefly algorithm, a differential evolution method, a particle swarm optimizer, and a biogeography-based optimizer. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic FA.