Showing papers on "Particle swarm optimization published in 2018"
01 Jan 2018
TL;DR: Its origin and background is introduced and the theory analysis of the PSO is carried out, which analyzes its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithms, multi-objective optimization PSO and its engineering applications.
Abstract: Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.
1,091 citations
TL;DR: A new wrapper feature selection approach is proposed based on Whale Optimization Algorithm based on the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process to search the optimal feature subsets for classification purposes.
534 citations
08 Jul 2018
TL;DR: Numerical results and non-parametric statistical significance tests indicate that the Coyote Optimization Algorithm is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.
Abstract: The behavior of natural phenomena has become one of the most popular sources for researchers to design optimization algorithms for scientific, computing and engineering fields. As a result, a lot of nature-inspired algorithms have been proposed in the last decades. Due to the numerous issues of the global optimization process, new algorithms are always welcome in this research field. This paper introduces the Coyote Optimization Algorithm (COA), which is a population based metaheuristic for optimization inspired on the canis latrans species. It contributes with a new algorithmic structure and mechanisms for balancing exploration and exploitation. A set of boundary constrained real parameter optimization benchmarks is tested and a comparative study with other nature-inspired metaheuristics is provided to investigate the performance of the COA. Numerical results and non-parametric statistical significance tests indicate that the COA is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.
369 citations
TL;DR: This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed, and shows that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.
309 citations
TL;DR: A wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm based on time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation.
Abstract: The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches.
304 citations
TL;DR: The solution results quality of this study show that the proposed HFPSO algorithm provides fast and reliable optimization solutions and outperforms others in unimodal, simple multi-modal, hybrid, and composition categories of computationally expensive numerical functions.
292 citations
TL;DR: Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP and the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events is studied.
Abstract: Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.
284 citations
01 Feb 2018
TL;DR: This paper proposes to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems, and demonstrates that compared to the canonical PSO-based and a state-of-the-art PSO variants for feature selection, the proposed CSO- based feature selection algorithm not only selects a much smaller number of features, but result in better classification performance as well.
Abstract: When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance.Please check the edit made in the article title Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large-scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to perform feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO-based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based feature selection algorithm not only selects a much smaller number of features, but result in better classification performance as well.
273 citations
TL;DR: The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces.
Abstract: This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness
267 citations
TL;DR: Particle swarm optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms as discussed by the authors.
Abstract: Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.
260 citations
TL;DR: In this paper, an alternative to physical relocation based on particle swarm optimization (PSO) connected modules is proposed, where the physical location of the modules remains unchanged, while its electrical connections are altered.
Abstract: For large photovoltaic power generation plants, number of panels are interconnected in series and parallel to form a photovoltaic (PV) array. In this configuration, partial shade will result in decrease in power output and introduce multiple peaks in the P–V curve. As a consequence, the modules in the array will deliver different row currents. Therefore, to maximize the power extraction from PV array, the panels need to be reconfigured for row current difference minimization. Row current minimization via Su Do Ku game theory do physical relocation of panels may cause laborious work and lengthy interconnecting ties. Hence, in this paper, an alternative to physical relocation based on particle swarm optimization (PSO) connected modules is proposed. In this method, the physical location of the modules remains unchanged, while its electrical connections are altered. Extensive simulations with different shade patterns are carried out and thorough analysis with the help of I–V , P–V curves is carried out to support the usefulness of the proposed method. The effectiveness of proposed PSO technique is evaluated via performance analysis based on energy saving and income generation. Further, a comprehensive comparison of various electrical array reconfiguration based is performed at the last to examine the suitability of proposed array reconfiguration method.
TL;DR: A novel coverage control algorithm based on Particle Swarm Optimization (PSO) is presented that can effectively improve coverage rate and reduce energy consumption in WSNs.
Abstract: Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.
TL;DR: The farmland fertility in problems with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely and the effectiveness of other algorithms decreases significantly with number of dimensions and the farmland fertility obtains better results than other algorithms.
01 Jan 2018
TL;DR: This chapter aims to review of all metaheuristics related issues by dividing metaheuristic algorithms according to metaphor based and non-metaphor based in order to differentiate between them in searching schemes and clarify how the metaphor based algorithms simulate the selected phenomenon behavior in the search area.
Abstract: Metaheuristic algorithms are computational intelligence paradigms especially used for sophisticated solving optimization problems. This chapter aims to review of all metaheuristics related issues. First, metaheuristic algorithms were divided according to metaphor based and non-metaphor based in order to differentiate between them in searching schemes and clarify how the metaphor based algorithms simulate the selected phenomenon behavior in the search area. The major algorithms in each metaphor subcategory are discussed including: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Water Waves Optimization (WWO), Clonal Selection Algorithm (CLONALG), Chemical Reaction Optimization (CRO), Harmony Search (HS), Sine Cosine Algorithm (SCA), Simulated Annealing (SA), Teaching–Learning-Based Optimization (TLBO), League Championship Algorithm (LCA), and others. Also, some non-metaphor based metaheuristics are explained as Tabu Search (TS), Variable Neighborhood Search (VNS). Second, different variants of metaheuristics are categorized into improved metaheuristics, adaptive, hybridized metaheuristics. Also, various examples are discussed. Third, a real-time case study “Welded Beam Design Problem” is solved with 10 different metaheuristics and the experimental results are statistically analyzed with non-parametric Friedman test in order to estimate the different performance of metaheuristics. Finally, limitation and new trends of metaheuristics are discussed. Besides, the chapter is accompanied with literature survey of existing metaheuristics with references for more details.
TL;DR: Extensive experiments on CEC′13/15 test suites and in the task of standard image segmentation validate the effectiveness and efficiency of the MPSO algorithm proposed in this paper.
Abstract: Particle swarm optimization (PSO) is a population based meta-heuristic search algorithm that has been widely applied to a variety of problems since its advent. In PSO, the inertial weight not only has a crucial effect on its convergence, but also plays an important role in balancing exploration and exploitation during the evolution. However, PSO is easily trapped into the local optima and premature convergence appears when applied to complex multimodal problems. To address these issues, we present a modified particle swarm optimization with chaos-based initialization and robust update mechanisms. On the one side, the Logistic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other side, the sigmoid-like inertia weight is formulated to make the PSO adaptively adopt the inertia weight between linearly decreasing and nonlinearly decreasing strategies in order to achieve better tradeoff between the exploration and exploitation. During this process, a maximal focus distance is formulated to measure the particle's aggregation degree. At the same time, the wavelet mutation is applied for the particles whose fitness value is less than that of the average so as to enhance the swarm diversity. In addition, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of MPSO. Extensive experiments on CEC′13/15 test suites and in the task of standard image segmentation validate the effectiveness and efficiency of the MPSO algorithm proposed in this paper.
TL;DR: This paper proposes a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation.
TL;DR: The proposed ELBS method provides optimal scheduling and load balancing for the mixing work robots by using the improved particle swarm optimization algorithm and a multiagent system to achieve the distributed scheduling of manufacturing cluster.
Abstract: Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.
TL;DR: An improved PSO variant, with enhanced leader, named as enhanced leader PSO (ELPSO) is used and results confirm that in most of the cases, ELPSO outperforms conventional PSO and a couple of other state of the art optimisation algorithms.
TL;DR: In this article, a comparative study of optimization techniques for identifying soil parameters in geotechnical engineering is presented, and the identification methodology with its 3 main parts, error function, search strategy, and identification procedure, is introduced and summarized.
Abstract: Summary
A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current optimization methods were reviewed and classified into 3 categories with an introduction to their basic principles and applications in geotechnical engineering. A comparative study on the identification of model parameters from a synthetic pressuremeter and an excavation tests was then performed by using 5 among the mostly common optimization methods, including genetic algorithms, particle swarm optimization, simulated annealing, the differential evolution algorithm and the artificial bee colony algorithm. The results demonstrated that the differential evolution had the strongest search ability but the slowest convergence speed. All the selected methods could reach approximate solutions with very small objective errors, but these solutions were different from the preset parameters. To improve the identification performance, an enhanced algorithm was developed by implementing the Nelder-Mead simplex method in a differential algorithm to accelerate the convergence speed with strong reliable search ability. The performance of the enhanced optimization algorithm was finally highlighted by identifying the Mohr-Coulomb parameters from the 2 same synthetic cases and from 2 real pressuremeter tests in sand, and ANICREEP parameters from 2 real pressuremeter tests in soft clay.
TL;DR: A hybrid PSO algorithm which employs an adaptive learning strategy (ALPSO) is developed in this paper, which performs much better than the others in more cases, on both convergence accuracy and convergence speed.
TL;DR: The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot.
Abstract: Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.
TL;DR: The two empirical results illustrate that the novel initial condition with dynamic weighted coefficients can better adjust to the features of electricity consumption data than the previous initial conditions and show the superiority of the newly proposed model over the benchmark models.
TL;DR: This paper proposes a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle Swarm optimization algorithm (SL-PSO), where the PSO and SL- PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model.
TL;DR: Results prove that the proposed Hybrid SCA-DE-based tracker can robustly track an arbitrary target in various challenging conditions than the other trackers and is very competitive compared to the state-of-the-art metaheuristic algorithms.
TL;DR: This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression based forecaster and a two-step hybrid parameters optimization method.
Abstract: This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.
02 Jul 2018
TL;DR: This paper designs a particle swarm optimization based energy-aware edge server placement algorithm that can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.
Abstract: Edge server placement problem is a hot topic in mobile edge computing. In this paper, we study the problem of energy-aware edge server placement and try to find a more effective placement scheme with low energy consumption. Then, we formulate the problem as a multi-objective optimization problem and devise a particle swarm optimization based energy-aware edge server placement algorithm to find the optimal solution. We evaluate the algorithm based on the real dataset from Shanghai Telecom and the results show our algorithm can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.
TL;DR: In this article, a particle swarm optimization-based variational mode decomposition method was proposed for fault detection in rotating machinery, which adopts the minimum mean envelope entropy to optimize the parameters (α$ and K$ ) in the existing variational decomposition.
Abstract: The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and mixed with abundant compounded background noise. To extract the potential excitations from the observed rotating machinery, signal demodulation and time–frequency analysis are indispensable. This work proposes a novel particle swarm optimization-based variational mode decomposition method, which adopts the minimum mean envelope entropy to optimize the parameters ( $\alpha$ and $K$ ) in the existing variational mode decomposition. The proposed fault-detection framework separated the observed vibration signals into a series of intrinsic modes. A certain number of the intrinsic modes are then selected by means of the Hilbert transform-based square envelope spectral kurtosis. Subsequently, in this study, the feature representations were reconstructed via the selected intrinsic modes; then, the envelope spectra of the real faulty conditions were generated in the rotating machinery. To verify the performance of the proposed method, a testbed platform of a gearbox with a combination of different faults was implemented. The experimental results demonstrated that the proposed method represented the patterns of the fault frequency more explicitly than the available empirical mode decomposition, the local mean decomposition, and the wavelet package transform method.
TL;DR: A balanceable fitness estimation method and a novel velocity update equation are presented, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs.
Abstract: Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform poorly when tackling many-objective optimization problems (MaOPs). This is mainly because the loss of selection pressure that occurs when updating the swarm. The number of nondominated individuals is substantially increased and the diversity maintenance mechanisms in MOPSOs always guide the particles to explore sparse regions of the search space. This behavior results in the final solutions being distributed loosely in objective space, but far away from the true Pareto-optimal front. To avoid the above scenario, this paper presents a balanceable fitness estimation method and a novel velocity update equation, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs. Moreover, an evolutionary search is further run on the external archive in order to provide another search pattern for evolution. The DTLZ and WFG test suites with 4–10 objectives are used to assess the performance of NMPSO. Our experiments indicate that NMPSO has superior performance over four current MOPSOs, and over four competitive multiobjective evolutionary algorithms (SPEA2-SDE, NSGA-III, MOEA/DD, and SRA), when solving most of the test problems adopted.
TL;DR: In this paper, the authors proposed a natural cubic-spline-guided Jaya algorithm (S-Jaya) for solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions.
Abstract: This paper proposes a novel model-free solution algorithm, the natural cubic-spline-guided Jaya algorithm (S-Jaya), for efficiently solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions. A photovoltaic (PV) system which controls the power generation with its operating voltage is considered. As the same as the generic Jaya algorithm, the S-Jaya is free of algorithm-specific parameters. A natural cubic-spline-based prediction model is incorporated into the iterative search process to guide the update of candidate solutions (operating voltage settings) in the S-Jaya and such extension is capable of improving the tracking performance. Simulation studies and experiments are conducted to validate the effectiveness of the proposed S-Jaya algorithm for better addressing PV MPPT problems considering a variety of partial-shading conditions. The performance of the proposed algorithm is benchmarked against the generic Jaya and the particle swarm optimization, which has been widely considered in the model-free MPPT, to demonstrate its advantages. Results of simulation studies and experiments demonstrate that the S-Jaya algorithm converges faster and provides a higher overall tracking efficiency.
TL;DR: In this paper, a novel chaotic bat algorithm (CBA) was proposed for multi-level thresholding in grayscale images using Otsu's between-class variance function.
Abstract: Multi-level thresholding is a helpful tool for several image segmentation applications Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA) Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321) Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives Therefore, it can be applied in complex image processing such as automatic target recognition