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Philippine Eagle Optimization Algorithm
Abstract
We propose the Philippine Eagle Optimization Algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 13 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.
... However, this can be computationally expensive and may still result in a local minimizer. To resolve this, the minimizer of the GCV score is obtained numerically using an evolutionary algorithm called Philippine Eagle Optimization Algorithm [8], which is a nature-inspired, meta-heuristic optimization technique that utilizes the hunting behavior of the Philippine Eagle. It employs three distinct global operators for its exploration strategy, and it also features an intensive local search during each iteration, resulting in a strong ability to obtain accurate minimizers. ...
Trend analysis plays an important role in infectious disease control. An analysis of the underlying trend in the number of cases or the mortality of a particular disease allows one to characterize its growth. Trend analysis may also be used to evaluate the effectiveness of an intervention to control the spread of an infectious disease. However, trends are often not readily observable because of noise in data that is commonly caused by random factors, short-term repeated patterns, or measurement error. In this paper, a smoothing technique that generalizes the Whittaker-Henderson method to infinite dimension and whose solution is represented by a polynomial is applied to extract the underlying trend in infectious disease data. The solution is obtained by projecting the problem to a finite-dimensional space using an orthonormal Sobolev polynomial basis obtained from Gram-Schmidt orthogonalization procedure and a smoothing parameter computed using the Philippine Eagle Optimization Algorithm, which is more efficient and consistent than a hybrid model used in earlier work. Because the trend is represented by the polynomial solution, extreme points, concavity, and periods when infectious disease cases are increasing or decreasing can be easily determined. Moreover, one can easily generate forecast of cases using the polynomial solution. This approach is applied in the analysis of trends, and in forecasting cases of different infectious diseases.
... From the early formulations of the genetic algorithm [15], state-of-the-art algorithms have been continuously proposed. Many of these are nature-inspired, which use the optimal processes of animal behavior [1], [4], [10], [40], evolutionary characteristics of organisms [6], [18], [24], [25], [33], or other physical processes [8], [11], [28], [31]. Most of these algorithms are built for unconstrained optimization problems and should be modified for constrained problems through handling techniques such as the penalty method [38], superiority of feasible points [7], -constrained selection scheme [35], gradient repair method [5], or stochastic ranking [32]. ...
p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Metaheuristic algorithms for constrained optimiza- tion problems have become popular because of their ease of use and capability to obtain global solutions. However, these population-based algorithms can be computationally expensive and may suffer from low accuracy due to the difficulty in obtaining feasible points. We present a novel algorithm, re- ferred to as SASS-CMODE, by integrating a modified Improved Multi-Operator Differential Evolution (IMODE) algorithm with the Self-Adaptive Spherical Search (SASS) method. IMODE is modified to make it suitable for solving constrained problems, leading to a new algorithm termed Constrained Multi-Operator Differential Evolution (CMODE). SASS-CMODE is capable of achieving solutions with high feasibility rate and high accuracy by utilizing SASS to identify good feasible points and CMODE to achieve accurate solutions with fewer function evaluations. To evaluate its performance, we test SASS-CMODE to 57 engi- neering problems. The results demonstrate its superiority over other state-of-the-art optimization algorithms. SASS-CMODE is also employed to solve a constrained optimization problem on identifying optimal levels of non-pharmaceutical interventions to control an epidemic, showcasing its versatility and applicability in real-world scenarios.</p
... From the early formulations of the genetic algorithm [15], state-of-the-art algorithms have been continuously proposed. Many of these are nature-inspired, which use the optimal processes of animal behavior [1], [4], [10], [40], evolutionary characteristics of organisms [6], [18], [24], [25], [33], or other physical processes [8], [11], [28], [31]. Most of these algorithms are built for unconstrained optimization problems and should be modified for constrained problems through handling techniques such as the penalty method [38], superiority of feasible points [7], -constrained selection scheme [35], gradient repair method [5], or stochastic ranking [32]. ...
p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Metaheuristic algorithms for constrained optimiza- tion problems have become popular because of their ease of use and capability to obtain global solutions. However, these population-based algorithms can be computationally expensive and may suffer from low accuracy due to the difficulty in obtaining feasible points. We present a novel algorithm, re- ferred to as SASS-CMODE, by integrating a modified Improved Multi-Operator Differential Evolution (IMODE) algorithm with the Self-Adaptive Spherical Search (SASS) method. IMODE is modified to make it suitable for solving constrained problems, leading to a new algorithm termed Constrained Multi-Operator Differential Evolution (CMODE). SASS-CMODE is capable of achieving solutions with high feasibility rate and high accuracy by utilizing SASS to identify good feasible points and CMODE to achieve accurate solutions with fewer function evaluations. To evaluate its performance, we test SASS-CMODE to 57 engi- neering problems. The results demonstrate its superiority over other state-of-the-art optimization algorithms. SASS-CMODE is also employed to solve a constrained optimization problem on identifying optimal levels of non-pharmaceutical interventions to control an epidemic, showcasing its versatility and applicability in real-world scenarios.</p
... The Philippine eagle optimization (PEO) algorithm is a population-based metaheuristic algorithm, stimulated by the flying, foraging and hunting behaviors of territorial Philippine eagle [33]. It is scientifically named as Pithecophaga jefferyi. ...
Nowadays, face recognition using video surveillance systems becomes one of the active research topics in security domains. Security plays a significant role in everyday life for secure and sustainable developments of smart cities. The conventional techniques provide efficient recognition results only when the faces are captured with complete face images. However, they suffer to handle large pose variation images extracted from video sequences. Therefore, to deal with this issue, this paper specially designed a multidimensional face recognition model to recognize faces under multiple pose variations and angles. Three face video databases, namely facesurv database, IARPA Janus benchmark database and McGill database, are utilized for experimental evaluation. The videos of these three databases are converted into number of image frames through background subtraction process. From the image frames, the large pose variation images with different angles are identified and selected to process further. The video recorded under dynamic environment conditions diminishes recognition performance, so the image frames are processed through several preprocessing pipelines. The preprocessed images are then fed into the proposed optimal mask region-based convolutional neural network with modified short-term memory (OMRCNN-MBiLSTM) model, which learns the facial patterns present in the images more efficiently. The feature vectors learned by the proposed classifier are matched with the input face database to determine the identity of the person. With the ability to handle multiview and large pose variations, the proposed model accurately recognizes faces. The simulation result manifests the superiority of proposed model over other existing methods.
... One of the strongest birds around the world is the Philippine eagle which has specific behaviors like foraging, flying, and hunting. The major features of the Philippine eagles are described below [25]; ...
Decentralized mobile ad hoc networks (MANET) is a self-organizing and adaptive method comprised of multiple mobile nodes. Due to the dynamic and flexible nature of network topology, wireless communication links face major issues based on trust and security. The researchers came to know that MANET suffers greatly from the quality of service (QoS). So, MANET recommends the employment of an intrusion detection system to identify intrusions, namely attacks, message eavesdropping, and untrustworthy routing. Intrusion detection is considered a significant task to enhance network security from unauthorized access. The main issue in MANET is the limited battery source of sensor nodes which causes communication blockage after a certain transmission range. Therefore, to offer continuous data transmission without any intrusion, in this article, we introduced a secure high-yielding carry-out approach “modified k-means Philippine eagle (MKMPE) approach.” Initially, for the determination of the ideal cluster head (CH) from the cluster group, the modified k-means algorithm (MKM) employs for communicating information to the destination node without any packet loss. The CH is selected according to the values of credits namely indirect, recent trust, and direct trust. Then, with the support of Philippine eagle (PE) optimization, the energy-efficient and secure paths are discovered to transfer the information with minimum delay. To examine the potential ability of the proposed MKMPE approach, various state-of-the-art methods are compared with the suggested method in terms of evaluation metrics, namely energy consumption, network lifetime, end-to-end delay, latency, packet delivery ratio, throughput, and detection rate. This proposed MKMPE approach achieved a maximum detection rate of about 78%, a throughput rate of 0.86 bps, and minimum latency of 0.09 s concerning a maximum time interval of 45 ms.
The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA’s fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine’s (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.
Accurate classification of high-dimensional biomedical data highly depends on the efficient recognition of the data's main features which can be used to assist diagnose related diseases. However, due to the existence of a large number of irrelevant or redundant features in biomedical data, classification approaches struggle to correctly identify patterns in data without a feature selection algorithm. Feature selection approaches seek to eliminate irrelevant and redundant features to maintain or enhance classification accuracy. In this paper, a new wrapper feature selection method is proposed based on the chimp optimization algorithm (ChOA) for biomedical data classification. The ChOA is a newly proposed metaheuristic algorithm whose capability for solving feature selection problems has not been investigated yet. Two binary variants of the ChoA are introduced for the feature selection problem. In the first approach, two transfer functions (S-shaped and V-shaped) are used to convert the continuous version of ChoA to binary. In addition to the transfer function, the crossover operator is utilized in the second approach to improve the ChOA's exploratory behavior. To validate the efficiency of the proposed approaches, five publicly available high-dimensional biomedical datasets, and a few datasets from different domains such as life, text, and image are employed. The proposed approaches were then compared with six well-known wrapper-based feature selection methods, including multi-objective genetic algorithm (GA), particle swarm optimization (PSO), Bat algorithm (BA), ant colony optimization (ACO), firefly algorithm (FA), and flower pollination (FP) algorithm, as well as two standard filter-based feature selection methods using three different classifiers. The experimental results demonstrate that the proposed approaches can effectively remove the least significant features and improve classification accuracy. The suggested wrapper feature selection techniques also outperform the GA, PSO, BA, ACO, FA, FP, and other existing methods in the terms of the number of selected genes, and classification accuracy in most cases.
Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.
This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities. Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE. On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird’s nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability. On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy. In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects. The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness.
These days, a sizable number of meta-heuristic algorithms are utilized to address many problems with numerous variables and huge complexity. One of the most popular swarm intelligence-based meta-heuristic methods is the chimp optimization algorithm inspired by chimps’ individual intelligence and sexual motivation in their group hunting. This paper proposes a weighted chimp optimization algorithm to tackle two main issues in large-scale numerical optimization problems, such as low convergence speed and local optima trapping to solve high-dimensional problems. The main difference between the weighted and standard chimp optimization algorithms is that a position-weighted equation is offered to enhance convergence speed and avoid local optima. Moreover, the balance between exploration and exploitation is carried out in the proposed method that is crucial in the swarm intelligence-based algorithms. The presented weighted chimp optimization algorithm method is evaluated in different conditions to prove that it is the best. For this purpose, a classical set of 30 unimodal, multimodal, and fixed-dimension multimodal benchmark functions is applied to investigate the pros and cons of characteristics of the weighted chimp optimization algorithm. Besides, the proposed algorithm is tested on the IEEE congress of evolutionary computation benchmark test functions. In order to shed more light on probing the performance of the weighted chimp optimization algorithm in large-scale numerical optimization and real-world problems, it is examined by 13 high-dimensional and ten real-world optimization problems. The results show that the suggested algorithm outperforms in terms of convergence speed, the probability of getting stuck in local minimums, exploration, and exploitation compared to state-of-the-art methods in the literature. Source codes are publicly available at https://se.mathworks.com/matlabcentral/fileexchange/99344-a-weighted-chimp-optimization-algorithm.
Electrical impedance tomography (EIT) is a noninvasive functional diagnostic technique that has been successfully applied to human lung and brain. EIT reconstruction is an ill-conditioned problem: the regularization parameters establish a trade-off between acceptable data fitting and acceptable solution stability. This trade-off is necessary to select the optimal regularization parameters for each data frame to continuously obtain high-quality images in real-time monitoring. However, using the objective regularization parameter selection method before each image reconstruction may reduce the reconstruction efficiency, and using heuristic selection for all images may cause significant quality degradation, thereby creating challenges in long-term clinical EIT monitoring. This study explores a robust EIT target adaptive differential iterative reconstruction algorithm that does not require the advance selection of the optimal regularization parameter to facilitate the clinical application of EIT long-term bedside monitoring. Specifically, second-order Taylor approximation expansion and Euler–Lagrange theorem were used to derive the conductivity differential iteration relationship. Furthermore, the reconstruction quality and generalization ability of the proposed algorithm were validated based on comparisons with the L-curve and the generalized cross-validation parameter selection methods through simulations, phantom experiments, and human lung recruitment data. Compared with the L-curve and generalized cross-validation methods, the TADI algorithm reduces the position error by 33.07% and 47.17%, the ringing by 26.49% and 32.69%, and the shape deformation by 18.34% and 19.40% on average, respectively. The results revealed that the TADI algorithm could achieve a significant improvement over the existing state-of-the-art methods in terms of stability, consistency, and efficiency.
This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.
Neutral delay differential equations (NDDEs) are differential equations containing time lags not only in the states but also in the state derivatives. NDDEs have applications in modeling physical and biological systems. An NDDE model may consist of several parameters, some of which can be determined using available data. Various numerical techniques have been studied to estimate parameters of mathematical models. In this study, parameter estimation is posed as an optimization problem. The use of heuristic algorithms in parameter estimation has gained popularity because of its ease of implementation, requiring only function evaluations. But to our knowledge, heuristic algorithms have never been employed in estimating parameters in NDDE models. In this work, we apply Genetic Algorithm with Multi-Parent Crossover (GA-MPC) to obtain parameter estimates of three NDDE models with a discrete delay. We compare the estimates to those obtained using standard heuristic algorithms. Results show that GA-MPC is capable of consistently identifying model parameters that provide a good fit of the model to the data.
Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired by chimps’ individual intelligence and sexual motivation in their group hunting. The preferable performance of ChOA has been approved among other well-known meta-heuristic algorithms. However, its continuous nature makes it unsuitable for solving binary problems. Therefore, this paper proposes a novel binary version of ChOA and attempts to prove that the transfer function is the most important part of binary algorithms. Therefore, four S-shaped and V-shaped transfer functions, as well as a novel binary approach, have been utilized to investigate the efficiency of binary ChOAs (BChOA) in terms of convergence speed and local minima avoidance. In this regard, forty-three unimodal, multimodal, and composite optimization functions and ten IEEE CEC06-2019 benchmark functions were utilized to evaluate the efficiency of BChOAs. Furthermore, to validate the performance of BChOAs, four newly proposed binary optimization algorithms were compared with eighteen novel state-of-the-art algorithms. The results indicate that both the novel binary approach and V-shaped transfer functions improve the efficiency of BChOAs in a statistically significant way.
The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.
Background
The Chimp Optimization Algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents’ insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation.
Objective
This paper introduces a Dynamic Lévy Flight (DLF) technique to smoothly and gradually transit the search agents from the exploration phase to the exploitation phase.
Methods
To investigate the efficiency of the DLFChOA, this paper evaluates the performance of DLFChOA on twenty-three standard benchmark functions, twenty challenging functions of CEC-2005, ten suit tests of IEEE CEC06-2019, and twelve real-world optimization problems. The results are compared to benchmark optimization algorithms, including CMA-ES, SHADE, ChOA, HGSO, LGWO and ALEP (as the best benchmark Lévy-based algorithms), and eighteen state-of-the-art algorithms (as the winners of the CEC2019, the GECCO2019, and the SEMCCO2019).
Result and conclusion
Among forty-three numerical test functions, DLFChOA and CMA-ES gain the first and second rank with thirty and eleven best results. In the 100-digit challenge, jDE100 with a score of 100 provides the best results, followed by DISHchain1e+12, and DLFChOA with a score of 85.68 is ranked fifth among eighteen state-of-the-art algorithms achieved the best score in seven out of ten problems. Finally, DLFChOA and CMA-ES respectively gain the best results in five and four real-world engineering problems.