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Coello, C.A.C.: An updated survey of GA-based multi-objective optimization techniques. ACM Computing Surveys 32(2), 109-143
Abstract
After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.
... For the test case examined, which will be further analyzed in the second part of the study, the objective functions considered are the Fire Safety Index (FSI) and the Authenticity Preservation Index (API), while cost constitutes in all test cases the constraint of the problem, depending on the available budget. Many algorithms have been presented for solving the multi-objective optimization problem [64,65]. In this study, the Nondominated Sorting Evolution Strategies II (NSES-II) algorithm [66] is employed for solving the two-objective problem at hand. ...
The preservation of cultural heritage structures includes, among others, an efficient fire protection design process. This engineering design process frequently generates critical decision making issues related to conflicts that involve the buildings’ authenticity preservation, the implementation of special fire protection measures and addressing the particular needs of such structures. However, conventional approaches based on prescriptive regulations are often problematic in such cases; on the contrary, Performance-Based (PB) approaches could successfully deal with such structures to deliver designs that satisfy an acceptable fire safety level, and at the same time minimize the cost and any interventions on the building’s appearance, to the extent that authenticity is a key demand. Thus, in this study the upgrade of the fire safety level of cultural heritage structures is expressed as a Multi-Criteria Decision Making (MCDM) problem. Accordingly, the Analytic Hierarchy Process (AHP) is incorporated into a new fire protection Selection and Resource (S&R) allocation model, aiming to assess both fire safety and authenticity preservation levels with reference to the protection measures selected. Furthermore, in this study two different multi-criteria optimization approaches are applied to generate optimized solutions of the fire safety upgrading scheme. In this first part of the study, the theoretical basis of the proposed S&R allocation model that relies on a MCDM problem and how to deal with is discussed, while in the second part the implementation of the proposed model is presented for two real-world test cases. More specifically, in this study the theoretical part of the multi-objective and the multi-disciplinary problems (belonging to the MCDM type of problems) is provided with respect to the problems’ description and the methods adopted for solving the corresponding problems.
A novel deterministic approach, based on pre-search of the radial set, is proposed for optimal reconfiguration of radial systems. This type of optimization consists in finding a spanning tree that optimizes a given objective function. On the other hand, the deterministic approach means that the best solution will always be found. Due to the combinatorial nature of the problem, exhaustive evaluation of all possible solutions is generally not feasible when analyzing medium to large scale systems because of the enormous computational time required. The proposed algorithm works on a topologically equivalent reduced system to improve performance when facing this huge challenge in the optimization of radial systems, which consists in obtaining the Radial Set of feasible solutions. The proposal was tested in the optimal reconfiguration of two electrical distribution systems taken from the scientific literature, obtaining results that demonstrate a promising potential to apply the proposed algorithm in real medium-sized industrial system.
In engineering, optimizing parameters often involves computationally expensive tasks, especially when dealing with multi-dimensional variables and multiple performance metrics. This falls under the category of multi-objective black-box optimization. To address this, we propose two optimization algorithms for low and medium-dimensional spaces, incorporating relaxation conditions for hyperplane segmentation. For the specific parameter optimization of HC-ARF, we employed a two-stage approach. It combines a BP neural network as a surrogate model with a hyperplane separation optimization algorithm. This method efficiently optimizes both confinement loss (CL) and birefringence, using a weighted sum approach to identify their Pareto sets. We validate the effectiveness and stability of the surrogate model by comparing it with traditional optimization algorithms. Exhaustive experiments confirm the superiority of this algorithm and the results show that our optimized structure achieves impressive performance metrics, including a loss of 0.8 dB/m, a birefringence of 2.2×10⁻⁴, and a critical bending radius of 0.5 cm under optimal parameters.
Metaheuristic algorithms have wide applicability, particularly in wireless sensor networks (WSNs), due to their superior skill in solving and optimizing many issues in different domains. However, WSNs suffer from several issues, such as deployment, localization, sink node placement, energy efficiency, and clustering. Unfortunately, these issues negatively affect the already limited energy of the WSNs; therefore, the need to employ metaheuristic algorithms is inevitable to alleviate the harm imposed by these issues on the lifespan and performance of the network. Some associated issues regarding WSNs are modelled as single and multi-objective optimization issues. Single-objective issues have one optimal solution, and the other has multiple desirable solutions that compete, the so-called non-dominated solutions. Several optimization strategies based on metaheuristic algorithms are available to address various types of optimization concerns relating to WSN deployment, localization, sink node placement, energy efficiency, and clustering. This review reports and discusses the literature research on single and multi-objective metaheuristics and their evaluation criteria, WSN architectures and definitions, and applications of metaheuristics in WSN deployment, localization, sink node placement, energy efficiency, and clustering. It also proposes definitions for these terms and reports on some ongoing difficulties linked to these topics. Furthermore, this review outlines the open issues, challenge paths, and future trends that can be applied to metaheuristic algorithms (single and multi-objective) and WSN difficulties, as well as the significant efforts that are necessary to improve WSN efficiency.
STATCOM is a source of controllable reactive current used to balance reactive power at power system nodes and manage reactive flow in transmission lines. Most prevalent efficient FACTS stand for Flexible AC Transmission Systems components is the Static Synchronous Compensator (STATCOM), which is frequently modeled as a controlled voltage source for reactive current regulation and increased stability of the power system. To improve the stability of the power grid, a unique robust control strategy for STATCOM controller design has been developed. The suggested scheme is characterized as an optimization problem, and the performance and stability of the controller are assessed using the robustness criteria. To change the controllers' coefficients, the proposed model is applied to each of these control systems, and used controller is a PID controller, The Pareto optimal solution is initially discovered using a modified version of the non-dominance-based genetic algorithm (NSGAII). Due to an increase in load and inadequate growth of the producing and transmission capacity, the operating limitations of a modernized integrated power system are getting tighter. Electricity networks occasionally experience small oscillations
Non-dominated Sorting Genetic Algorithm (NSGA)-II is applied for multiple objective machine part cell formation problem. Minimization of cell load variation and minimization of total moves are considered as two conflicting objectives. Pareto optimal solutions are obtained for some test problems. Results are compared with those obtained by other methods. The comparison reveals that Pareto optimal fronts obtained are better than reported and reveal the strength of NSGA-II algorithm suited for integer multiple objective cell formation problems in Cellular Manufacturing Systems.
Adaptive penalty function methods (APFMs) are promising constraints handling techniques. In an APFM, a penalty parameter which balances constrains’ violations and objective function values is adaptively adjusted. This work modifies an APFM that uses a near feasibility threshold (NFT), a portion around the feasible region where infeasible solutions are considered as good ones, for constrained multiobjective optimization. The modified APFM with five different settings of NFT is embedded in a prominent multiobjective evolutionary algorithm based on decomposition, MOEA/D. This brings in five constrained variants of the base algorithm, denoted by CMOEA/D-TAP1 to CMOEA/D-TAP5. These variants are tested on well-known constrained multiobjective benchmark test suits, the CTP series and the CF series. The proposed variants are compared with four best performing algorithms through HV metric (hyper volume metric) statistics for CTP series, and with seven state-of-the-art algorithms through Wilcoxon rank sum test employed to mean values of both HV and IGD (inverted generational distance matric) metrics for CF series. Simulation results reflect that overall performance of the newly introduced variants is better than the competitors for the taken benchmark test suits.
Infrared (IR) thermal camouflage and management are deeply desirable in the field of military and astronomy. While IR compatible with laser camouflage technology is extensively studied to counter modern detection systems, most existing strategies for visible light camouflage focus on color matching, which is not suitable for scenarios requiring transparency. In this work, we propose an optically transparent metamaterial with multi-band compatible camouflage capability based on the inverse design. The metamaterial consists of Ag grating, Si3N4 dielectric spacer layer, Ag reflection layer, and Si3N4 anti-reflective layer. An ideal multi-band compatible spectrum is involved in the inverse design algorithm. Calculated results demonstrate high transmittance (T0.38-0.78µm = 0.70) in the visible region, low reflectance (R1.55µm = 0.01) in laser working wavelength, high reflectance (R3-5µm = 0.86 and R8-14µm = 0.92) in the dual-band atmospheric window, and high emissivity (ɛ5-8µm = 0.61) for the non-atmospheric window. The radiative heat flux in the detected band is 31W/m² and 201W/m² respectively. Furthermore, the incident and polarized insensitivity of the proposed metamaterial supports applicability for practical situations. This work, emphasizes an effective strategy for conducting optically transparent design with compatible IR-laser camouflage as well as radiative cooling properties by an automated design approach.
An auto-tuning framework for GPU devices is presented for tuning application kernels of OpenCL. The GPU tuner employs multi-objective optimization methodology to improve the performance and power consumption of applications. It efficiently explores a user defined solution space comprising of possible tunable algorithmic and hardware counter variations through code transformations. The methodology targets GPU code tuning situations where performance and energy consumption are critical. The proposed framework is evaluated for 2D convolution kernels. It utilizes a non-dominated sorting Genetic Algorithm with hardware power sensor data for application code transformation through code rewrite and validation. Various algorithmic variations such as loop unrolling, caching, workgroup size and memory utilization are applied. The final Pareto optimal configurations code utilized around 30% less power and 4% faster execution time. The analysis shows the convergence of optimization, and 45% improvement in standard deviation.
Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.
Most optimization problems naturally have several objectives, usually in conflict with each other. The problems with two or three objective functions are referred to as Multi-Objective Problems (MOP). However, many real-world applications often involve four or more objectives, which are commonly recognized as many-objective optimization problems (MaOP). Multi and many-objective algorithms have a great application in engineering science. This study addresses a complete and updated review of the literature for multi and many-objective problems and discusses 32 more important algorithms in detail. Afterward, the ZDT and DLTZ benchmark problems for multi-objective test problems are reviewed. All methods have been studied under recent state-of-the-art quality measures. Moreover, we discuss the historical roots of multi-objective optimization, the motivation to use evolutionary algorithms, and the most popular techniques currently in use.
The problems of a 7-degree of freedom (DOF) manipulator with rapid and continuous response to uncertain fast-flying objects are addressed: 1) how to effectively solve trajectory planning of the 7-DOF manipulator with multiple criteria; and 2) how to make the 7-DOF manipulator realize the rapid and continuous response to uncertain fast-flying objects. In the proposed approach, based on the trajectory parameterization of the 7-DOF manipulator, a multiobjective teaching-learning-based optimization (MOTLBO) algorithm is adopted to find a close representation of the Pareto optimal set rather than a single solution. As such, an optimal solution can be chosen as digital knowledge information. A new methodology based on a knowledge base representing and learning the operation environment, that is, skill digitization, is presented, which enables the 7-DOF manipulator to realize the rapid and continuous response skill. Simulation and practical testing results of a ping-pong robot validate the feasibility and effectiveness of the proposed approach, in which the online trajectory generation spends only around 1 ms.
The preservation of cultural heritage structures is a complex procedure that should incorporate an efficient fire protection design to reduce fire risk. However, during this process, engineers frequently face critical decision-making-type questions concerning the implementation of the fire protection measures in order to both achieve an acceptable fire safety level and at the same time minimize the cost and the alteration of the building, in the cases that authenticity is a key demand. In the present study, the fire safety upgrading of historic structures is expressed as a multi-criteria decision-making (MCDM) problem. In this context, the Analytic Hierarchy Process (AHP) is used, to assess both the overall fire safety level and the authenticity preservation level of a structure in terms of the fire protection measures implemented. Furthermore, the Nondominated Sorting Evolution Strategies (NSES-II) solution algorithm is incorporated into the proposed multi-objective optimization approach to generate Pareto optimal solutions. Finally, the efficiency of the proposed framework is presented for the case of Monastery of Simonos Petra, Mount Athos, Greece. When the preservation of the authenticity is considered as an objective within the design process, the need for the implementation of more sophisticated and customized fire protection measures can lead to a significant increase between 18.4% and 183.4% regarding the total cost, depending on the pursued safety level.KeywordsEvolutionary algorithmsMulti-criteria decision-makingFire protectionHistoric buildingAuthenticityMonastery of Simonos Petra
The rational use of energy systems is one of the main discussions in sustainability in the 21st century. Water pumping systems are one of the most significant consumers of electricity in urban systems, whether for urban water supply, sewage, or use in vertical buildings. Thus, this work aims to present Industry 4.0 (I4.0) technologies applied in buildings’ water pumping systems, focusing on energy efficiency, supervision, and control of the pumping system. The work involves four steps: (i) identifying the existing I4.0 technologies and (ii) mapping the possibilities of applying Industry 4.0 technologies in building pumping systems. The study includes the analysis of (16) articles published in journals between 2018 and June 2021 to identify I4.0 technologies cited in the publications. It identified and grouped eighteen (18) technologies based on twenty-two (22) terms observed in the papers. The study classified the identified technologies into three possible applications in a building water pumping system. The applications include: (i) directly applicable, (ii) partially applicable, and (iii) application not yet identified. Therefore, the study presents the advantages of I4.0 technologies developed primarily for the industry sector, also applicable in residential building water pumping systems. These technologies’ benefits include energy efficiency, user control, a reduction from periods of failure of the pumping system (maintenance), water quality, and moving towards Intelligent Pumping or Pumping 4.0.
We provide a comprehensive overview of the literature of algorithmic approaches for multiobjective mixed‐integer and integer linear optimization problems. More precisely, we categorize and display exact methods for multiobjective linear problems with integer variables for computing the entire set of nondominated images. Our review lists 108 articles and is intended to serve as a reference for all researchers who are familiar with basic concepts of multiobjective optimization and who have an interest in getting a thorough view on the state‐of‐the‐art in multiobjective mixed‐integer programming.
Simultaneous optimization of several competing objectives requires increasing the capability of optimization algorithms. This paper proposes the multi-objective moth swarm algorithm, for the first time, to solve various multi-objective problems. In the proposed algorithm, a new definition for pathfinder moths and moonlight was proposed to enhance the synchronization capability as well as to maintain a good spread of non-dominated solutions. In addition, the crowding-distance mechanism was employed to select the most efficient solutions within the population. This mechanism indicates the distribution of non-dominated solutions around a particular non-dominated solution. Accordingly, a set of non-dominated solutions obtained by the proposed multi-objective algorithm is kept in an archive to be used later for improving its exploratory capability. The capability of the proposed MOMSA was investigated by a set of multi-objective benchmark problems having 7 to 30 dimensions. The results were compared with three well-known meta-heuristics of multi-objective evolutionary algorithm based on decomposition (MOEA/D), Pareto envelope-based selection algorithm II (PESA-II), and multi-objective ant lion optimizer (MOALO). Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were employed for comparison purposes. The qualitative and quantitative results indicated the superior performance and the higher capability of the proposed MOMSA algorithm over the other algorithms. The MOMSA algorithm with the average values of CPU time = 2771 s, GD = 0.138, S = 0.063, Δ = 1.053, and MS = 0.878 proved to be a robust and reliable model for multi-objective optimization.
The proliferation of wireless sensor network (WSNs) applications span different domains of life, including medicine, engineering, industry, agriculture, and military. A notable part of research pertaining to WSNs relates to metaheuristic algorithms, implemented to address difficulties in the deployment of these networks. Due to robust and cost effective optimization ability, these algorithms efficiently optimize sensor locations for maximum coverage and extended energy consumption. This chapter presents the definitions of metaheuristic intelligence, wireless sensor network, and their respective types. Also, a wide range of scientific research works that include improving the performance of wireless sensor networks in terms of deployment, localization, and energy using optimization algorithms. Finally, the evaluation criteria for deployment and localization in wireless sensor networks are introduced.
Several complex engineering design problems have multiple, conflicting objectives and constraints that are nonlinear, along with mixed discrete and continuous design variables; these problems are inherently difficult to solve. This chapter presents a novel hybrid approach to find solutions to a constrained multi-objective mixed-discrete nonlinear programming problem that combines a two-branch genetic algorithm as a global search tool with a gradient-based approach for the local search. Hybridizing two algorithms can provide a search approach that outperforms the individual algorithms; however, hybridizing the two algorithms, in the traditional way, often does not offer advantages other than the computational efficiency of the gradient-based algorithms and global exploring capability of the evolutionary-based algorithms. The approach here presents a hybridization approach combining genetic algorithm and a gradient-based approach with improved information sharing between the two algorithms. The hybrid approach is implemented to solve three engineering design problems of different complexities to demonstrate the effectiveness of the approach in solving constrained multi-objective mixed-discrete nonlinear programming problems.
The medical enhancements have been improved to increase the life span of mankind. High penetration of Wireless Sensor Network in medical field helped the doctors to diagnose the patience in accurate manner and prescribe the medicines accordingly. In this modern Era, many people have permanent implant like face maker and it is life threatening to keep changing this body enhancement and it is need to have a system in place to increase the functionality of the Wireless Body Sensors. The important parameter which drags the battery energy and reduces its life span is path loss, and transmission loss. This paper proposes enhanced life time improvement for wireless body sensor networks using optimal clustering and advanced path selection protocol in short Energy Proficient Reliable Multi-hop Routing protocol. The EPMR designed in two stages, the first stage start with the collection of data from all body sensors is done through optimal clustering technique based Modified Conditional Spider Optimization (M-CSO) which avoids continuous individual transmission of data by the sensing nodes to base station in multi- hop distance (this stage the transmission loss is eliminated or reduced) hence the protocol concentrate to reduce transmission losses and the second stage implements the discovery of distance is done through by deploying Modified Flower Bee Algorithm (M-FBA), this stage the protocol finds best suitable and shortest next node to transmit the data. The finding of this paper is simulated using Math lab Simulink and verified through Network Simulator – 2 (NS-2) tools for validation and real time implementation of the results. The result obtained by this proposed method suggests that it has enhanced the sensor life term in many folds in comparisons with existing methodologies.
Due to its proven efficiency and computational speed, the most recent developed meta-heuristic optimization methods are widely used to better integrate renewable distributed generation (RDG) into the electricity grid. The main objective of this paper is to obtain a better knowledge of current trends in meta-heuristics applied to optimally integrate RDGs to the distribution network. This is a review of well known meta-heuristic approaches, used to solve the problem of optimal renewable distributed generation allocation planning (ORDGAP). In this context, some research gaps were mentioned, and recommendations were proposed to expand the scope of research in this field.
A state observer plays a vital role in the design of state feedback neuromodulation schemes used to prevent and treat neurological or psychiatric disorders. This paper aims to design a state observer to reconstruct all unmeasured states of the computational network model of neural populations that replicates patterns seen on the electroencephalogram by using the model inputs and outputs, as the theoretical basis for designing state feedback neuromodulation clinical schemes. The feasibility problem of linear matrix inequality conditions, which is the most important one for observer design of the computational network model of neural populations, is solved by using the input–output stability theory and the Lurie system theory. The observer matrices of the designed observer are formed by the optimal solution of the linear matrix inequality conditions. An illustrative example shows that the observer can simultaneously reproduce internal state variables of normal and lesion populations of the computational network model of neural populations under the background of focal origin brain dysfunction, and the designed observer has certain robustness toward input uncertainty and measurement noise. To the best of our knowledge, no observers have previously been designed for the computational network model of neural populations. The design of state feedback neuromodulation schemes based on the computational network model of neural populations is a new direction in the field of computational neuroscience.
In recent years, ransomware has emerged as a new malware epidemic that creates havoc on the Internet. It infiltrates a victim system or network and encrypts all personal files or the whole system using a variety of encryption techniques. Such techniques prevent users from accessing files or the system until the required amount of ransom is paid. In this paper, we introduce an optimal, yet effective classification scheme, called ERAND (Ensemble RANsomware Defense), to defend against ransomware. ERAND operates on an optimal feature space to yield the best possible accuracy for the ransomware class as a whole as well as for each variant of the family.
Since ages nations have been trying to improve their military effectiveness by adopting various measures. Having an anticipatory system, which can not only accurately predict the most probable location for the enemy's base station but also finds the best route to that point, will lead to improved military operations. This paper aims to propose an integrated framework for developing an efficient anticipatory system. In the first phase of the framework, it proposes Anticipatory Multi objective Cuckoo Search (AMOCS) algorithm to identify the best probable location for deployment of enemy forces. For the second phase a hybrid CS-ACO algorithm is developed for obtaining the most suitable path to the location identified in the first phase. To test the proposed system, satellite image of regions of different terrain types namely plain/desert and mountainous respectively, are chosen. Experimental results demonstrate that the system makes accurate predictions.
Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.
Optimization is a field where extensive research has been conducted over the last decades. Many types of problems have been addressed, and many types of algorithms have been developed, while their range of applications is continuously growing. The chapter is divided into two parts; in the first part, the life-cycle cost analysis is used as an assessment tool for designs obtained by means of prescriptive and performance-based optimum design methodologies. The prescriptive designs are obtained through a single-objective formulation, where the initial construction cost is the objective to be minimized, while the performance-based designs are obtained through a two-objective formulation where the life-cycle cost is considered as an additional objective also to be minimized. In the second part of the chapter, the problem of inspection of structures and routing of the inspection crews following an earthquake in densely populated metropolitan areas is studied. A model is proposed and a decision support system is developed to aid local authorities in optimally assigning inspectors to critical infrastructures. A combined particle swarm – ant colony optimization based framework is implemented, which proves to be an instance of a successful application of the philosophy of bounded rationality and decentralized decision-making for solving global optimization problems.
In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms.
Decision support tools are essential to help the management of industrial systems at different levels: strategic to size the system; tactical to plan activities or assign resources; operational to schedule activities. We present a generic and modular decision support tool to solve different problems of planning, assignment, scheduling, or lot-sizing. Our tool uses a hybridization between a metaheuristic and a list algorithm. The specification of the considered problem is taken into account in the list algorithm. Several tactical and operational problems have been solved with our tool: a problem of planning activities with resources assignment for hospital systems, a lot-sizing and scheduling problem taking into account the setup time for a textile application and for a plastic injection problem, and a scheduling problem with precedence constraints. At the strategic level, this tool can also be used as part of Industry 4.0 to design reconfigurable manufacturing systems. This paper summarizes some problems solved with the proposed tool and presents its evolution.
In this paper, a multi-objective probabilistic design optimisation approach is presented for reliability and robustness analysis of composite structures and demonstrated on a mono-omega-stringer stiffened panel. The proposed approach utilises a global surrogate model of the composite structure while accounting for uncertainties in material properties as well as geometry. Unlike the multi-level optimisation approach which freezes some parameters at each level, the proposed approach allows for all parameters to change at the same time and hence ensures global optimum solutions in the given parameter design space (for both probabilistic and deterministic optimisations) within a certain degree of accuracy. The proposed approach is used in this study to conduct extensive multi-objective probabilistic and deterministic optimisations (without considering safety factors) on a mono-stringer stiffened panel. In particular, a global surrogate model is developed utilising the computational power of a high-performance computing facility. The inputs of the surrogate model are the omega-stringer geometry and the mechanical properties of the composite material, while the outputs are the fundamental linear buckling load (LBL) and the nonlinear post-buckling strength (NPS). LBL and NPS are obtained via detailed parametric finite element models of the mono-stringer stiffened panel; in the nonlinear model, the interface between the skin and the omega-stringer is modelled via cohesive elements to allow for debonding in the post-buckled regime. Extensive multi-objective optimisations are conducted on the surrogate model using deterministic and probabilistic approaches to examine the omega-stringer geometric parameters mostly affecting the system robustness and reliability. The differences between deterministic and probabilistic designs are highlighted as well.
A parameter-free optimization technique is applied in Quasi-Newton’s method for solving unconstrained multiobjective optimization problems. The components of the Hessian matrix are constructed using q-derivative, which is positive definite at every iteration. The step-length is computed by an Armijo-like rule which is responsible to escape the point from local minimum to global minimum at every iteration due to q-derivative. Further, the rate of convergence is proved as a superlinear in a local neighborhood of a minimum point based on q-derivative. Finally, the numerical experiments show better performance.
To reduce healthcare costs and improve human well-being, a promising technology known as wireless body area networks (WBANs) has recently emerged. It is comprised of various on-body as well as implanted sensors which seamlessly monitor the physiological characteristics of the human body. The information is heterogeneous in nature, requires different QoS factors. The information may be classified as delay-sensitive, reliability-sensitive, critical, and routine. On-time delivery and minimum losses are the main QoS-factors required to transmit the captured information. Various researchers have work to provide the required QoS, and some have also considered the other constraints due to the characteristics and texture of the human body. In this research work, we have discussed the communication architecture of WBANS along with the various challenges of WBANS. Furthermore, we have classified and discussed the existing QoS-aware data dissemination mechanisms. In the end, a comparative study of existing QoS-aware data dissemination mechanisms highlights the pros and cons of each mechanism.
In recent years, a number of recombination operators have been proposed for multiobjective evolutionary algorithms (MOEAs). One kind of recombination operators is designed based on the Gaussian process model. However, this approach only uses one standard Gaussian process model with fixed variance, which may not work well for solving various multiobjective optimization problems (MOPs). To alleviate this problem, this paper introduces a decomposition-based multiobjective evolutionary optimization with adaptive multiple Gaussian process models, aiming to provide a more effective heuristic search for various MOPs. For selecting a more suitable Gaussian process model, an adaptive selection strategy is designed by using the performance enhancements on a number of decomposed subproblems. In this way, our proposed algorithm owns more search patterns and is able to produce more diversified solutions. The performance of our algorithm is validated when solving some well-known F, UF, and WFG test instances, and the experiments confirm that our algorithm shows some superiorities over six competitive MOEAs.
Some recent research shows that in multi-objective evolutionary algorithms (MOEAs), mating with similar individuals can improve the quality of new solutions and accelerate the convergence of algorithms. Based on the above finding, some clustering-based mating restriction strategies are proposed. However, those clustering algorithms are not suitable for the population with non-convex structures. Therefore, it may fail to detect population structure in different evolutionary stages. To solve this problem, we propose a normalized hypervolume-based mating transformation strategy (NMTS). In NMTS, the population structure is detected by K-nearest-neighbor graph and spectral clustering before and after the mating transformation condition, respectively. And the parent solutions are chosen according to the founded population structure. The proposed algorithm has been applied to a number of test instances with complex Pareto optimal solution sets or Pareto fronts, and compared with some state-of-the-art MOEAs. The results have demonstrated its advantages over other algorithms.
This study describes the shape synthesis of a metallic flywheel using a non-dominated sorting Jaya algorithm. Generally, the flywheel is used to store the kinetic energy in the machines. Kinetic energy is an essential parameter to measure flywheel performance and can be improved by the optimal shape of the flywheel. In order to the optimal shape of the flywheel, the multi-objective problem with the maximization of the kinetic energy and minimization of von Mises stresses is formulated under appropriate design constraints using the cubic B-spline curve. A flowchart is proposed to solve the two-point boundary value differential equation for the calculation of von Mises stress at each point between the inner and outer radii of the flywheel. The design variables are represented by the control points of the cubic B-spline curve. A posteriori approach-based algorithm as non-dominated sorting Jaya algorithm (NSJaya) is used to solve the formulated optimization problem. This algorithm is based on the concepts of crowding distance and non-dominated sorting approach and gives the optimal Pareto set. The proposed method is applied to the flywheel of the agricultural thresher. The performance of the proposed algorithm is compared with that of non-dominated sorting genetic algorithm (NSGA-II) using hyper-volume performance metric. It is found that the NSJaya algorithm gives better results compared to NSGA-II and a posteriori approach-based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and Jaya. The optimal Pareto set for the optimal shape of the flywheel is calculated and outlined in this paper. The designer can choose any solution from the Pareto set for the optimal shape of the flywheel. ANSYS parameter design language (APDL) software is used for the validation of the von Mises stresses in the optimized shapes of the flywheel.
Cable yarding is the most commonly used technique for harvesting timber from steep terrain in central Europe. During the planning process, one important task is to define the cable road layout. This means that the harvesting technology and cable road location must be specified for a given timber parcel. Although managers must minimize harvesting costs, it is even more important that such work on forests reduces the potential for damage to the residual stand and ensures that environmental conditions remain suitable for regeneration. However, current methods are geared only toward minimizing harvesting costs and are computationally demanding and difficult to handle for the end user. These limitations hinder broad application of such methods. Further, the underlying productivity models used for cost estimation do not cover all conditions of an area and they cannot be applied over a whole harvesting area. To overcome these shortcomings, we present: (1) a multiobjective optimization approach that leads to realistic, practicable results that consider multiple conflicting design objectives, and (2) a concept for an easy-to-use application. We compare the practical applicability and performance of the results achieved with multiobjective optimization with those achieved with single-objective (cost-minimal) optimization. Based on these points, we then present and discuss a concept for a user-friendly implementation. The model was tested on two sites in Switzerland. The study produced the following major findings: (1) Single-objective alternatives have no practical relevance, whereas multiobjective alternatives are preferable in real-world applications and lead to realistic solutions; (2) the solution process for a planning unit should include analysis of the Pareto frontier; and (3) results can only be made available within a useful period of time by parallelizing computing operations.
Despite recent interest in multiobjective integer programming, few algorithms exist for solving biobjective mixed integer programs. We present such an algorithm: the boxed line method. For one of its variants, we prove that the number of single-objective integer programs solved is bounded by a linear function of the number of nondominated line segments in the nondominated frontier. This is the first such complexity result. An extensive computational study demonstrates that the box line method is also efficient in practice and that it outperforms existing algorithms on a diverse set of instances.
Cloud computing systems often have two conflicting objective, maximizing service performance and minimizing computing cost. The excellent task scheduling and resource allocation strategies can improve the cost/utility ratio efficiently. It is an NP-hard problem to optimize task scheduling of precedence-constrained parallel tasks represented by a directed acyclic graph (DAG) on cloud system. In order to address this problem, a chemical reaction multi-objective optimization algorithm (CRMO) is proposed in this paper. The CRMO executes four chemical reaction operators (named On-wall Ineffective Collision, Inter-molecular Ineffective Collision, Decomposition, and Synthesis) for cloud tasks DAG scheduling. The experiment results show that CRMO can produce outstanding cloud task scheduling solutions set.
This paper describes the use of multiobjective genetic algorithms
(MOGAs) in the design of a multivariable control system for a gas
turbine engine. It is shown how the MOGA confers an immediate advantage
over conventional multiobjective optimization methods by evolving a
family of Pareto-optimal solutions allowing the control engineer to
examine the trade-offs between the different design objectives. In
addition, the paper demonstrates how the genetic algorithm can be used
to search in both controller structure and parameter space thereby
offering a potentially more general approach to optimization in
controller design than traditional numerical methods
Design of optimal control system by use of a multicriteria genetic algorithm (GA) is presented. A new problem formulation and a new GA solution technique using the Pareto set concept is developed. The performance index of a linear control system is usually the time integral of the sum of quadratic forms xTQx and uTRu, where the choice of the weighting matrices Q and R are almost always heuristic in nature. Thus a re-formulation of the optimal control system design as a multicriteria optimization task is developed here which avoids use of Q and R. Design of the optimal control system is considered as a feedback search procedure where a new design solution is assigned a fitness value for the GA by a reference to the previously obtained Pareto optimal solution set. A numerical control design example and simulation results are presented.
This paper presents a method for optimizing the design of reinforced concrete beams subject to a specified set of constraints. A new model of optimization is proposed, leading to more realistic and practical designs. As there are an infinite number of possible beam dimensions and reinforcement ratios that yield the same moment of resistance, it becomes difficult to achieve the least-cost design by conventional iterative methods. We present a method based upon a search technique using genetic algorithms. Several applications show how our system provides more realistic designs than other methods based on mathematical programming techniques. Also, we show our results of experimenting with several representation schemes for the genetic algorithm, and the methodology that we used to adjust its parameters — i.e. population size, crossover and mutation rates and maximum number of generations—so that it produces a reasonable answer in a short period of time. A prototype of this system is currently being tested at our school, to see its potential use as a tool for real-world applications.
In this work, a multiobjective genetic algorithm is applied in the identification of polynomial models for a real non-linear system. The approach is shown to allow multiple performance, complexity, and validity criteria to influence the selection of candidate model structures, leading to the production of various preferable alternatives. The various models produced by the algorithm enable a better informed selection of the final identified model.
In many structural design tasks the designer's goal is to minimize and/or maximize several functions simultaneously. This situation is formulated as a multicriterion optimization problem. Since optimization tasks in structural design are often modelled by means of non-linear programming, a multicriterion approach ot this programming is discussed in the paper. The problem is formulated as follows: find a vector of design variables which satisfies constraints and optimizes a vector function which represents several noncomparable criteria.
Existing and potential applications of multi-optimization techniques to structural design are reviewed. Two approaches are available to formulate a multiobjective structural design problem. The first approach starts with a classical design, say minimize weight subject to cost, reliability, risk and other constraints; and then some of the quantities included in the constraints, in particular cost and reliability, are used to define additional objectives.
This paper introduces a multiobjective optimization tool called MOSES (Multiobjective Optimization of Systems in the Engineering Sciences). This tool is a convenient testbed for analyzing the performance of new and existing multicriteria optimization techniques, and it is an effective engineering design tool. Two new multiobjective optimization techniques based on the genetic algorithm (GA) are introduced, and two engineering design problems are solved using them. These methods are based in the concept of min-max optimum, and can produce the Pareto set and the best trade-off among the objectives. The results produced by these approaches are compared to those produced with other mathematical programming techniques and GA-based approaches, showing the new techniques' capability to generate better trade-offs than the approaches previously reported in the literature.
A model of single stage, multiattribute decisionmaking and trade-off weight determination under uncertainty is presented and examined. Several behaviorally relevant extensions of the standard decision analysis paradigm are generalized by the model and an inverse decision aiding feature is included that permits the use of directly expressed preferences among the alternatives. Parameter values, i. e. , lowest level attribute utility scores and trade-off weights, are described by set membership. Procedures are presented for ranking the alternatives and determining trade-off weights if the preference information gleaned from utility score assessment, trade-off weight assessment, and directly expressed preferences among the alternatives is consistent. 11 refs.
Linear programming deals with problems such as (see [ 4], [ 5]): to maximize a linear function (forming a vector x) constrained by m + n linear inequalities.
An accelerating increase in linear programming applications to industrial problems has made it virtually impossible to keep abreast of them, not only because of their number (and diversity) but also because of the conditions under which many are carried out. Industrial (and governmental) secrecy is often present. Other conditions also bar access to ascertainment and assessment of the pattern of applications. Lack of a tradition for publication is one. Failure to ascertain the general significance of particular findings is another, as is discouragement arising from the fact that similar applications have previously been published by others. Immediate remedies are not available for these difficulties. Presumably conventions such as this will help, over a period of time, by encouraging informal contacts between interested persons.
A talk on “industrial applications of linear programming” must be altered to suit these circumstances. In place of a survey or evaluation of industrial studies, two broad issues which are relevant to all such applications will be discussed. These are, (1) use of linear programming models as guides to data collection and (2) analysis (and prognosis) of fruitful areas of additional research, especially those which appear to have been opened by industrial applications.
Genetic search derives its computational advantage from an intrinsic pattern recognition capability. Patterns or schemata associated with a high level of fitness are rapidly identified and reproduced at a near-exponential growth rate through generations of simulated evolution. This highly exploitative search process has been shown to be extremely effective in searching for schema that represent an optimum, requiring only that an appropriate measure of fitness be defined. This exploitative pattern recognition process is also at work in another biological system-the immune system which recognizes antigens foreign to the system and generates antibodies to combat the growth of these antigens. The present paper describes key elements of how the functioning of the immune system can be modeled in the context of genetic search, and its applicability for handling constrained genetic search. Results from this simulation are compared with those obtained from the more traditional approach of handling constraints in genetic search, viz. through the use of a penalty function formulation.
The present paper describes an implementation of genetic search methods in multicriterion optimal designs of structural systems with a mix of continuous, integer and discrete design variables. Two distinct strategies to simultaneously generate a family of Pareto optimal designs are presented in the paper. These strategies stem from a consideration of the natural analogue, wherein distinct species of life forms share the available resources of an environment for sustenance. The efficacy of these solution strategies are examined in the context of representative structural optimization problems with multiple objective criteria and with varying dimensionality as determined by the number of design variables and constraints.
The common application areas of Genetic Algorithms (GAs) have been to single criterion difficult optimization problems. The GA selection mechanism is often dependent upon a single valued scalar objective funtion. In this paper, we present results of a modified distance method. The distance method was proposed earlier by us, for solving multiple criteria problems with GAs. The Pareto set estimation method, which is fundamental to multicriteria analysis, is used to perform the multicriteria optimization using GAs. First, the Pareto set is found out from the population of the initial generation of the GA. The fitness of a new solution, is calculated by a distance measure with reference to the Pareto set of the previous runs. We calculate the distances of a solution from all the Pareto solutions found since the previous run, but the minimum of these distances is taken under consideration while evaluating the fitness of the solution. Thus the GA tries to maximize the distance of future Pareto solutions from present Pareto solutions in the positive Pareto space of the given problem. Here we modify distance method, by using an improved algorithm to assign and make use of the latent potential of the Pareto solutions which are found during the runs. Two detailed numerical examples and computer generated results are also presented.
The target vector criterion as the vector distance between a set of desired responses and a set of really obtained responses of a multivariate function was combined with the genetic algorithm to improve simultaneously six properties of a biochemical test strip for human blood glucose determination as a function of twelve chemical and technological parameters. The advantage of the genetic algorithm in comparison with other search techniques became obvious for the search in this twelve-dimensional variables space with up to seven bit resolution, i.e., up to 1.9325 search positions. The results obtained by target vector optimization on the basis of the genetic search technique were critically compared with the results obtained by a prediction with classical and non-linear partial least-squares regression and realized in laboratory and industrial verification experiments. In this way advantages and disadvantages of deductive and inductive polyoptimization strategies could be discussed theoretically and with respect to experimental results.
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications.
In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics.
Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.
Bradford Books imprint
DISSERTATION (PH.D.)--THE UNIVERSITY OF MICHIGAN Dissertation Abstracts International,
David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
In investment, it is highly desirable to maximize return or profit within a given risk level. Constructing a portfolio of investments to optimize the outcome is among the most significant financial decisions facing individuals and institutions. Essentially the standard portfolio optimization problem is to identify the optimal allocation of limited resources among a limited set of investments. Optimality is measured using a tradeoff between perceived risk and expected return. Expected future returns are based on historical data. Risk is measured by the variance of those historical returns. In this project, Genetic Algorithm is explored to tackle the multi-objective portfolio problem. GA is inspired from evolution process in which species evolve to improve themselves. This technique has received much attention in the past few years due to its powerful optimization and structure determining capabilities. ELECTRICAL and ELECTRONIC ENGINEERING
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