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This paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a well-distributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions. [ABSTRACT FROM AUTHOR]

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm. [ABSTRACT FROM AUTHOR]

Purpose Adaptive equalization plays an important role in digital communication to reduce the distortions due to inter-symbol interference. An adaptive filter is used as an equalizer model in channel equalization. An adaptive algorithm is the heart of the adaptive filter which finds the optimum coefficients of the filter. The choice of the adaptive algorithm improves the convergence rate and minimizes the mean square error (MSE). This paper aims to propose a cat swarm optimization (CSO)-based adaptive algorithm and its modification to improve the performance of a channel equalizer.Design/methodology/approach The input digital training data are transmitted through different channel conditions. A linear transversal filter is used as a channel and equalizer model. The equalizer coefficients are trained by the proposed simplified cat swarm optimization (SCSO) algorithm to find the estimated digital training data.Findings The performance of the proposed SCSO algorithm is compared with particle swarm optimization (PSO)-based channel equalization. The improvement in convergence rate and MSE is verified under linear and nonlinear channel conditions with different delay spreads. The optimum parameters of the SCSO are found using simulation-based sensitivity analysis.Originality/value This paper analyzes a CSO algorithm for adaptive channel equalization and proposes a SCSO algorithm to identify the optimum coefficients of a transversal equalizer. The seeking mode process is simplified in the proposed SCSO to achieve better performance in channel equalization. The proposed SCSO algorithm guarantees minimum MSE in all independent runs, whereas in PSO, few misses are possible. [ABSTRACT FROM AUTHOR]

Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging problem. Many papers have proposed different Multi-Objective Evolutionary Algorithms to solve the lack of the convergence and diversity in many-objective problems. One of the more promising approaches uses a set of reference points to discriminate the solutions and guide the search process. However, this approach was incorporated mainly in Multi-Objective Evolutionary Algorithms, and there are just some few promising adaptations of Particle Swarm Optimization approaches for effectively tackling many-objective problems regarding convergence and diversity. Thus, this paper proposes a practical and efficient Many-Objective Particle Swarm Optimization algorithm for solving many-objective problems. Our proposal uses a set of reference points dynamically determined according to the search process, allowing the algorithm to converge to the Pareto front, but maintaining the diversity of the Pareto front. Our experimental results demonstrate superior or similar performance when compared to other state-of-art algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles’ exploration and adaptation of the search direction. [ABSTRACT FROM AUTHOR]

Many optimization problems in reality have become more and more complex, which promote the research on the improvement of different optimization algorithms. The particle swarm optimization (PSO) algorithm has been proved to be an effective tool to solve various kinds of optimization problems. However, for the basic PSO, the updating strategy is mainly aims to learn the global best, and it often suffers premature convergence as well as performs poorly on many complex optimization problems, especially for multimodal problems. A hybrid PSO algorithm which employs an adaptive learning strategy (ALPSO) is developed in this paper. In ALPSO, we employ a self-learning based candidate generation strategy to ensure the exploration ability, and a competitive learning based prediction strategy to guarantee exploitation of the algorithm. To balance the exploration ability and the exploitation ability well, we design a tolerance based search direction adjustment mechanism. The experimental results on 40 benchmark test functions demonstrate that, compared with five representative PSO algorithms, ALPSO performs much better than the others in more cases, on both convergence accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

In the past two decades, multi-objective optimization has attracted increasing interests in the evolutionary computation community, and a variety of multi-objective optimization algorithms have been proposed on the basis of different population based meta-heuristics, where the family of multi-objective particle swarm optimization is among the most representative ones. While the performance of most existing multi-objective particle swarm optimization algorithms largely depends on the global or personal best particles stored in an external archive, in this paper, we propose 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. The performance of the proposed competitive multi-objective particle swarm optimizer is verified by benchmark comparisons with several state-of-the-art multi-objective optimizers, including three multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed. [ABSTRACT FROM AUTHOR]

Swarm intelligence optimization algorithms are mainstream algorithms for solving complex optimization problems. Among these algorithms, the particle swarm optimization (PSO) algorithm has the advantages of fast computation speed and few parameters. However, PSO is prone to premature convergence. To solve this problem, we develop a new PSO algorithm (RPSOLF) by combining the characteristics of random learning mechanism and Levy flight. The RPSOLF algorithm increases the diversity of the population by learning from random particles and random walks in Levy flight. On the one hand, we carry out a large number of numerical experiments on benchmark test functions, and compare these results with the PSO algorithm with Levy flight (PSOLF) algorithm and other PSO variants in previous reports. The results show that the optimal solution can be found faster and more efficiently by the RPSOLF algorithm. On the other hand, the RPSOLF algorithm can also be applied to optimize the Lennard-Jones clusters, and the results indicate that the algorithm obtains the optimal structure (2–60 atoms) with an extraordinary high efficiency. In summary, RPSOLF algorithm proposed in our paper is proved to be an extremely effective tool for global optimization. [ABSTRACT FROM AUTHOR]

Purpose – To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues. Design/methodology/approach – In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability. Findings – This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH. Originality/value – Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems.Design/methodology/approach A modified QPSO algorithm is designed.Findings The modified QPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic inverse problems. More specially, the experimental results as reported on different case studies demonstrate that the proposed method can find better final optimal solution at an early stage of the iterating process (uses less iterations) as compared to other tested optimal algorithms.Originality/value The modifications include the design of a new position updating formula, the introduction of a new mutation strategy and a dynamic control parameter to intensify the convergence speed of the algorithm. [ABSTRACT FROM AUTHOR]

It is prevalent that the Pareto-based dominant framework is inefficient in non-dominated sorting because the performance sharply deteriorates when there are numerous weak dominance relations. In order to address this issue, the paper presents a novel ranking strategy called Global Margin Ranking (GMR) which deploys the position information of individuals in objective space to gain the margin of dominance throughout the population. The method not only considers the distribution of population, but also incorporates the associated information of individuals, without incurring user-defined parameters. Moreover, in view of the challenges faced by Multi-objective Particle Swarm Optimization (MOPSO) in selection of gBest and pBest , we present an innovative strategy for selecting gBest and pBest by integrating the GMR and the individual’s density information. We compare the GMR method with a variety of other ranking methods in terms of the distribution of ranks, the ranking landscape and convergence of the evolutionary process. The relatively extensive experimental results on some benchmark functions show that MOPSO/GMR performs better than those specialized MOEAs. [ABSTRACT FROM AUTHOR]

Abstract: Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. To tackle the challenges of dynamic optimization, several strategies have been proposed to enhance the performance of PSO, and have gained success on various dynamic optimization problems. But there still exist some issues in dynamic optimization which need to be studied carefully, i.e. the robustness of the algorithm to problems of various dynamic features. In this paper, a new multi-strategy ensemble particle swarm optimization (MEPSO) for dynamic optimization is proposed. In MEPSO, all particles are divided into two parts, denoted as part I and part II, respectively. Two new strategies, Gaussian local search and differential mutation, are introduced into these two parts, respectively. Experimental analyses reveal that the mechanisms used in part I can enhance the convergence ability of the algorithm, while mechanisms used in part II can extend the searching area of the particle population to avoid being trapped into the local optimum, and can enhance the ability of catching up with the changing optimum in dynamic environments. The whole algorithm has few parameters that need to be tuned, and all of them are not sensitive to problems. We compared MEPSO with other PSOs, including MQSO, PHPSO and Standard PSO with re-initialization, on moving peaks Benchmark and dynamic Rastrigin function. The experimental results show that MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does. [Copyright &y& Elsevier]

Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than 2 decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this paper, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem; named BNC-PSO ( B ayesian N etwork C onstruction algorithm using PSO ). Edge inserting/deleting is employed in the algorithm to make the particles have the ability to achieve the optimal solution, while a cycle removing procedure is used to prevent the generation of invalid solutions. Then, the theorem of Markov chain is used to prove the global convergence of our proposed algorithm. Finally, some experiments are designed to evaluate the performance of the proposed PSO-based algorithm. Experimental results indicate that BNC-PSO is worthy of being studied in the field of BNs construction. Meanwhile, it can significantly increase nearly 15% in the scoring metric values, comparing with other optimization-based algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized form of quantum-inspired particle swarm optimization (QPSO) with cellular structured population (called cQPSO) for keeping the population diversity and balancing the global and local search. The cQPSO is further improved by re-designing the local attractor in the sub-population (called cQPSO -lbest ) in order to accelerate the diffusion of the best solution and thus enhance the performance of cQPSO. The particles in cQPSO and cQPSO -lbest are distributed in a two-dimensional (2D) grid and only allowed to interact with their neighbors according to the specified neighborhood, which plays a role in exploiting the search space inside the neighborhood. The overlapping particles work for delivering the information among the nearest neighborhoods acting as exploring the search space with diffusion of solutions during the evolutionary process. Theoretical studies are made to analyze the global convergence of cPSO and cQPSO- lbest based on the theory of probabilistic metric space. We systematically investigate the performance of cQPSO -lbest on 42 benchmark functions with different properties (including unimodal, multimodal, separated, shifted, rotated, noisy, and mis-scaled) and compare with a set of PSO variants with different topologies and swarm-based evolutionary algorithms (EAs). The experimental results demonstrate the better performance of cQPSO -lbest . Moreover, two real-world problems, which are two-dimensional (2D) IIR digital filter design and economic dispatch (ED) problem from power systems area, are used to evaluate cQPSO- lbest and the experimental results verified the advantages of cQPSO- lbest . [ABSTRACT FROM AUTHOR]

Although the particle swarm optimization (PSO) algorithm has been widely used to solve many real world problems, it is likely to suffer entrapment in local optima when addressing multimodal optimization problems. In this paper, we report a novel hybrid optimization algorithm called crisscross search particle swarm optimization (CSPSO), which is different from PSO and its variants in that each particle is directly represented by pbest . The population of particles in CSPSO is updated by modified PSO as well as crisscross search optimization (CSO) in sequence within each iteration. CSO is incorporated as an evolutionary catalytic agent that has powerful capability of searching for pbest s of high quality, therefore accelerating the global convergence of PSO. CSO enhances PSO by two search operators, namely horizontal crossover and vertical crossover. The horizontal crossover further enhances PSO's global convergence ability while the vertical crossover can enhance swarm diversity. Several benchmark functions are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that CSPSO outperforms other state-of-the-art PSO variants significantly in terms of global search ability and convergence speed on almost all of the benchmark functions tested. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic mentoring scheme along with a self-regulation scheme have been incorporated in the standard Particle Swarm Optimization (PSO) algorithm to empower the searching particles with human-like characteristics. The algorithm is referred to as a Dynamic Mentoring and Self-Regulation based Particle Swarm Optimization (DMeSR-PSO) algorithm. Based on their experiences, the particles are divided into three groups, viz., the mentor group, the mentee group and the independent learner group where the number of particles in each group is dynamically changing in every iteration. In human learning psychology, mentoring is regarded as a powerful and effective learning process and independent learners are the ones who do not need mentoring and are capable of performing self-regulation of their own knowledge. Therefore, the particles in each of the above three groups have different learning strategies for their velocity updates where the mentors are equipped with a strong self-belief based search, the mentees are taking guidance from the mentors and the independent learners employ self-perception strategy. The DMeSR-PSO algorithm has been extensively evaluated using the simple unimodal and multimodal benchmark functions from CEC2005, more complex shifted and rotated benchmark functions fromCEC2013 and also based on eight real- world problems from CEC2011. The results have been compared with six state-of-the-art PSO variants and five meta-heuristic algorithms for the CEC2005 problems. Further, a comparative analysis on CEC2013 benchmark functions with different PSO variants has also been presented. Finally, DMeSR-PSO's performance on the real-world problems is compared with the top two algorithms from the CEC2011 competition. The results indicate that the proposed learning strategies help DMeSR-PSO to achieve faster convergence and provide better solutions in most of the problems with a 95% confidence level, yielding an effective optimization algorithm for real-world applications. [ABSTRACT FROM AUTHOR]

Recently, much progress has been made on particle swarm optimization (PSO). A number of works have been devoted to analyzing the convergence of the underlying algorithms. Nevertheless, in most cases, rather simplified hypotheses are used. For example, it often assumes that the swarm has only one particle. In addition, more often than not, the variables and the points of attraction are assumed to remain constant throughout the optimization process. In reality, such assumptions are often violated. Moreover, there are no rigorous rates of convergence results available to date for the particle swarm, to the best of our knowledge. In this paper, we consider a general form of PSO algorithms, and analyze asymptotic properties of the algorithms using stochastic approximation methods. We introduce four coefficients and rewrite the PSO procedure as a stochastic approximation type iterative algorithm. Then we analyze its convergence using weak convergence method. It is proved that a suitably scaled sequence of swarms converge to the solution of an ordinary differential equation. We also establish certain stability results. Moreover, convergence rates are ascertained by using weak convergence method. A centered and scaled sequence of the estimation errors is shown to have a diffusion limit. [ABSTRACT FROM AUTHOR]

This paper presents a novel variant of particle swarm optimizers (PSOs) that we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P id SO), which uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles’ local and global best positions information. This strategy enables P id SO to improve the diversity of swarm in a bid to discourage premature convergence and to perform a global search over the entire search space more efficiently. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that P id SO enhances the diversity of the swarm and features better search effectiveness and efficiency in solving most multimodal optimization problems when compared with other recent variants of PSOs and evolutionary optimization algorithms such as integral-controlled PSO (ICPSO), PID-controlled PSO (PIDCPSO), comprehensive learning PSO (CLPSO), I-population covariance matrix adaptation evolution strategy (IPOP-CMA-ES), and a multi algorithm genetically adaptive method for single objective optimization (AMALGAM-SO), and so on. Additionally, it has been observed that P id SO is able to achieve comparatively better success rates and success performances though it is more complex, and that the performance of P id SO is promoted by selecting proper law based controllers. Consequently, P id SO offers a new solution to real engineering optimization designs of industrial systems. [ABSTRACT FROM AUTHOR]

This paper proposes an information sharing mechanism (ISM) to improve the performance of particle swarm optimization (PSO). The ISM allows each particle to share its best search information, so that all the other particles can take advantage of the shared information by communicating with it. In this way, the particles could enhance the mutual interaction with the others sufficiently and heighten their search ability greatly by using the search information of the whole swarm. Also, a competitive and cooperative (CC) operator is designed for a particle to utilize the shared information in a proper and efficient way. As the ISM share the search information among all the particles, it is an appropriate way to mix up information of the whole swarm for a better exploration of the landscape. Therefore, the competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems. The satisfactory performance of CCPSO-ISM is evaluated by comparing it with other variants of PSOs on a set of 16 global optimization functions. Moreover, the effectiveness and efficiency of CCPSO-ISM is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality. [ABSTRACT FROM AUTHOR]

Generating timetables for an institution is a challenging and time consuming task due to different demands on the overall structure of the timetable. In this paper, a new hybrid method which is a combination of a great deluge and artificial bee colony algorithm (INMGD-ABC) is proposed to address the university timetabling problem. Artificial bee colony algorithm (ABC) is a population based method that has been introduced in recent years and has proven successful in solving various optimization problems effectively. However, as with many search based approaches, there exist weaknesses in the exploration and exploitation abilities which tend to induce slow convergence of the overall search process. Therefore, hybridization is proposed to compensate for the identified weaknesses of the ABC. Also, inspired from imperialist competitive algorithms, an assimilation policy is implemented in order to improve the global exploration ability of the ABC algorithm. In addition, Nelder-Mead simplex search method is incorporated within the great deluge algorithm (NMGD) with the aim of enhancing the exploitation ability of the hybrid method in fine-tuning the problem search region. The proposed method is tested on two differing benchmark datasets i.e. examination and course timetabling datasets. A statistical analysis t-test has been conducted and shows the performance of the proposed approach as significantly better than basic ABC algorithm. Finally, the experimental results are compared against state-of-the art methods in the literature, with results obtained that are competitive and in certain cases achieving some of the current best results to those in the literature. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with a finite-time particle swarm optimization algorithm for odor source localization. First, a continuous-time finite-time particle swarm optimization (FPSO) algorithm is developed based on the continuous-time model of the particle swarm optimization (PSO) algorithm. Since the introduction of a nonlinear damping item, the proposed continuous-time FPSO algorithm can converge over a finite-time interval. Furthermore, in order to enhance its exploration capability, a tuning parameter is introduced into the proposed continuous-time FPSO algorithm. The algorithm’s finite-time convergence is analyzed by using the Lyapunov approach. Second, the discrete-time FPSO algorithm is obtained by using a given dicretization scheme. The corresponding convergence condition is derived by using a linear matrix inequality (LMI) approach. Finally, the features and performance of the proposed FPSO algorithm are illustrated by using two ill-posed functions and twenty-five benchmark functions, respectively. In numerical simulation results, the problem of odor source localization is presented to validate the effectiveness of the proposed FPSO algorithm. [Copyright &y& Elsevier]

Abstract: In recent years, particle swarm optimization (PSO) algorithm has been used to solve global optimization problems. This algorithm is widely used as an effective optimization tool in various applications. However, traditional PSO consists of only two searching layers and thus often results in premature convergence into the local minima. Thus, multi-layer particle swarm optimization (MLPSO) is proposed in this paper to improve the performance of traditional PSO by increasing the two layers of swarms to multiple layers. The MLPSO strategy increases the diversity of searching swarms to improve its performance when solving complex problems. The experiment indicates that the novel approach improves the final results and the convergence speed. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) is a heuristic optimization technique which was inspired by flocking and swarming behavior of birds and insects. Same as other swarm intelligent methods, this algorithm also has its own disadvantages, such as premature convergence and rapid loss of diversity. In this paper, a new optimization method based on the combination of PSO and two novel operators is introduced in order to increase the exploration capability of the PSO algorithm (HEPSO). The first operator is inspired by the multi-crossover mechanism of the genetic algorithm, and the second operator uses the bee colony mechanism to update the position of the particles. Various values for probabilities are examined to find a trade-off for the PSO, multi-crossover formulation, and bee colony operator. The performance of the hybrid algorithm is tested using several well-known benchmark functions. The comparative study confirms that HEPSO is a promising global optimization algorithm and superior to the recent variants of PSO in terms of accuracy, speed, robustness, and efficiency. [Copyright &y& Elsevier]

Abstract: Particle Swarm Optimization (PSO) has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes a hybrid PSO algorithm, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions, including rotated multimodal and shifted high-dimensional problems. Comparison results show that DNSPSO obtains a promising performance on the majority of the test problems. [Copyright &y& Elsevier]

This paper proposes a particle swarm optimization (PSO) based particle filter (PF) tracking framework, the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously incorporates the newest observations into the proposal distribution in the update stage. In the proposed approach, likelihood measure functions involving multiple features are presented to enhance the performance of model fitting. Furthermore, a PSO algorithm throughout the tracking process self-adaptively adjusts the multi-feature weights. There are three main contributions. Firstly, the PSO algorithm is fused into the PF framework, which can efficiently alleviate the particles degeneracy phenomenon. Secondly, an effective convergence criterion for the PSO algorithm is explored, which can avoid particles getting stuck in local minima and maintain greater particle diversity. Finally, a multi-feature weight self-adjusting strategy is proposed, which can significantly improve the tracking robustness and accuracy. Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance. [ABSTRACT FROM AUTHOR]

Abstract: Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. Although it has been shown to perform well in finding optimal solutions for many optimization problems, there has so far been little theoretical analysis on its convergence and performance. This paper presents a convergence analysis and performance evaluation of the QPSO algorithm and it also proposes two variants of the QPSO algorithm. First, we investigate in detail the convergence of the QPSO algorithm on a probabilistic metric space and prove that the QPSO algorithm is a form of contraction mapping and can converge to the global optimum. This is the first time that the theory of probabilistic metric spaces has been employed to analyze a stochastic optimization algorithm. We provided a new definition for the convergence rate of a stochastic algorithm as well as definitions for three types of convergence according to the correlations between the convergence rate and the objective function values. With these definitions, the effectiveness of the QPSO is evaluated by computing and analyzing the time complexity and the convergence rate of the algorithm. Then, the QPSO with random mean best position (QPSO-RM) and the QPSO with ranking operator (QPSO-RO) are proposed as two improvements of the QPSO algorithm. Finally, some empirical studies on popular benchmark functions are performed in order to make a full performance evaluation and comparison between QPSO, QPSO-RM, QPSO-RO and other variants of PSO. [Copyright &y& Elsevier]

Abstract: In this paper, a hybrid of algorithms for electromagnetism-like mechanisms (EM) and particle swarm optimisation (PSO), called HEMPSO, is proposed for use in designing a functional-link-based Petri recurrent fuzzy neural system (FLPRFNS) for nonlinear system control. The FLPRFNS has a functional link-based orthogonal basis function fuzzy consequent and a Petri layer to eliminate the redundant fuzzy rule for each input calculation. In addition, the FLPRFNS is trained by the proposed hybrid algorithm. The main innovation is that the random-neighbourhood local search is replaced by a PSO algorithm with an instant-update strategy for particle information. Each particle updates its information instantaneously and in this way receives the best current information. Thus, HEMPSO combines the advantages of multiple-agent-based searching, global optimisation, and rapid convergence. Simulation results confirm that HEMPSO can be used to perform global optimisation and offers the advantage of rapid convergence; they also indicate that the FLPRFNS exhibits high accuracy. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) is a heuristic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking. The canonical PSO has the disadvantage of premature convergence. Several improved PSO versions do well in keeping the diversity of the particles during the searching process, but at the expense of rapid convergence. This paper proposes an example-based learning PSO (ELPSO) to overcome these shortcomings by keeping a balance between swarm diversity and convergence speed. Inspired by a social phenomenon that multiple good examples can guide a crowd towards making progress, ELPSO uses an example set of multiple global best particles to update the positions of the particles. In this study, the particles of the example set were selected from the best particles and updated by the better particles in the first-in-first-out order in each iteration. The particles in the example set are different, and are usually of high quality in terms of the target optimization function. ELPSO has better diversity and convergence speed than single-gbest and non-gbest PSO algorithms, which is proved by mathematical and numerical results. Finally, computational experiments on benchmark problems show that ELPSO outperforms all of the tested PSO algorithms in terms of both solution quality and convergence time. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems. [Copyright &y& Elsevier]

Abstract: In this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks. [Copyright &y& Elsevier]

Abstract: This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on “smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel PSO algorithm, referred to as SFIPSO (Scale-free fully informed particle swarm optimization). In the proposed algorithm a modified Barabási–Albert (BA) model is used as a self-organizing construction mechanism, in order to adaptively generate the population topology exhibiting scale-free property. The swarm population is divided into two subpopulations: the active particles and the inactive particles. The former fly around the solution space to find the global optima; whereas the latter are iteratively activated by the active particles via attaching to them, according to their own degrees, fitness values, and spatial positions. Therefore, the topology will be gradually generated as the construction process and the optimization process progress synchronously. Moreover, the cognitive effect and the social effect on the variance of a particle’s velocity vector are distributed by its “contextual fitness” value, and the social effect is further distributed via a time-varying weighted fully informed mechanism that originated from . It is proved by the results of comparative experiments carried out on eight benchmark test functions that the scale-free population topology construction mechanism and the weighted fully informed learning strategy can provide the swarm population with stronger diversity during the convergent process. As a result, SFIPSO obtained success rate of 100% on all of the eight test functions. Furthermore, SFIPSO also yielded good-quality solutions, especially on multimodal test functions. We further test the network properties of the generated population topology. The results prove that (1) the degree distribution of the topology follows power-law, therefore exhibits scale-free property, and (2) the topology exhibits “disassortative mixing” property, which can be interpreted as an important condition for the reinforcement of population diversity. [Copyright &y& Elsevier]

Abstract: Several variants of the particle swarm optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization (MO) problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that govern the convergence behavior of the algorithm to the optimal Pareto front in the objective function space. Computer simulations over benchmark MO problems have also been provided to substantiate the theoretical derivations. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems. [Copyright &y& Elsevier]