Diversity enhanced particle swarm optimization with neighborhood search
TL;DR: A hybrid PSO algorithm is proposed, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities.
About: This article is published in Information Sciences.The article was published on 2013-02-01. It has received 366 citations till now. The article focuses on the topics: Multi-swarm optimization & Metaheuristic.
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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
Cites methods from "Diversity enhanced particle swarm o..."
...2015b), neighborhood search mechanism (Wang et al. 2013), collision-avoiding mech-...
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...…2002), Bayesian optimization model (Monson and Seppi 2005), chemical reaction optimization (Li et al. 2015b), neighborhood search mechanism (Wang et al. 2013), collision-avoiding mechanism (Blackwell and Bentley 2002), information sharing mechanism (Li et al. 2015a), local search technique…...
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TL;DR: This survey presented a comprehensive investigation of PSO, including its modifications, extensions, and applications to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology.
Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
836 citations
Additional excerpts
...[91] proposed a hybrid PSO algorithm called...
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TL;DR: This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO), which performs well on low-dimensional problems and is promising for solving large-scale problems as well.
566 citations
TL;DR: This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm and presents some potential areas for future study.
Abstract: This paper reviews recent studies on the Particle Swarm Optimization PSO algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.
532 citations
Cites methods from "Diversity enhanced particle swarm o..."
...An algorithm called “Diversity enhanced with Neighborhood Search Particle Swarm Optimization,” DNSPSO, was proposed (Wang et al., 2013) in which the explorative behavior was controlled by enhancing the diversity of the particles in the swarm....
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TL;DR: A novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks based on the simulation of cooperative behavior of social-spiders, and is compared to other well-known evolutionary methods.
Abstract: Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.
427 citations
References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
24,053 citations
01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
11,224 citations
"Diversity enhanced particle swarm o..." refers background in this paper
...In the past decades, several variant swarm intelligence-based algorithms have been proposed to solve complex benchmark and real-world optimization problems, e.g., Particle Swarm Optimization (PSO) [29], Ant Colony Optimization (ACO) [14], Artificial Bee Colony (ABC) [27], Cat Swarm Optimization [7], etc. Due to PSO’s simple concept, easy implementation yet effectiveness, it has become popular in evolutionary optimization community....
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..., Particle Swarm Optimization (PSO) [29], Ant Colony Optimization (ACO) [14], Artificial Bee Colony (ABC) [27], Cat Swarm Optimization [7], etc....
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Journal Article•
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
Abstract: While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.
10,306 citations
"Diversity enhanced particle swarm o..." refers methods in this paper
...To compare the performance differences among DNSPSO and the other four PSO algorithms, we conduct a Wilcoxon signed-rank test [12,19]....
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04 May 1998
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.
9,373 citations
"Diversity enhanced particle swarm o..." refers background or methods in this paper
...Shi and Eberhart [43] introduced a parameter called inertia weight w for the classical PSO....
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...Hu and Eberhart [25] used dynamic neighborhood PSO to solve multi-objective optimization problems....
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...The parameter w, called inertia factor, which is used to balance the global and local search abilities of particles [43], rand1ij and rand2ij are two uniform random numbers generated independently within the range of [0,1], c1 and c2 are two learning factors which control the influence of the social and cognitive components, and t = 1, 2, ....
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...Hu and Eberhart [25] updated the neighborhood of each particle by dynamically selecting m particles that are the nearest to the current particle....
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...During a search process, each particle is attracted by its previous best particle (pbest) and the global best particle (gbest) as follows [43]....
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