An adaptive mutation operator for particle swarm optimization

Copyright @ 2008 MICParticle swarm optimization (PSO) is an effcient tool for optimization and search problems. However, it is easy to betrapped into local optima due to its in-formation sharing mechanism. Many research works have shown that mutation operators can help PSO prevent prema- ture convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compared for PSO. An adaptive mutation operator is designed. Experimental results show that these mutation operators can greatly enhance the performance of PSO. The adaptive mutation operator shows great advantages over non-adaptive mutation operators on a set of benchmark test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

An Overview of Particle Swarm Optimization Variants

AbstractParticle swarm optimization (PSO) is a stochastic algorithm used for the optimization problems proposed by Kennedy [1] in 1995. It is a very good technique for the optimization problems. But still there is a drawback in the PSO is that it stuck in the local minima. To improve the performance of PSO, the researchers proposed the different variants of PSO. Some researchers try to improve it by improving initialization of the swarm. Some of them introduce the new parameters like constriction coefficient and inertia weight. Some researchers define the different method of inertia weight to improve the performance of PSO. Some researchers work on the global and local best particles by introducing the mutation operators in the PSO. In this paper, we will see the different variants of PSO with respect to initialization, inertia weight and mutation operators

Multimodal estimation of distribution algorithms

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima

Particle Swarm Optimization (PSO) Variants with Triangular Mutation

Particle swarm optimization (PSO) is a stochastic algorithm used for the optimization problems proposed by Kenned in 1995. It is a very good technique for optimization problems. However, there is a drawback - it stuck in the local minima. To improve the performance of PSO, many researchers proposed different variants of PSO. Some of the efforts include improving the initialization of the swarm, introduce new parameters - constriction coefficient and inertia weight, define the different method of inertia weight to improve the performance of PSO, and modifying the global and local best particles by introducing the mutation operators in the PSO. In this paper, we will see the different variants of PSO with respect to initialization, inertia weight and mutation operators. We also proposed a new PSO technique using triangular mutation. The new technique is tested on the benchmarked functions. The results show better performance when compared to four previous PSO variants

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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