A Novel Dynamic Particle Swarm Optimization Algorithm Based on Chaotic Mutation | IEEE Conference Publication | IEEE Xplore

A Novel Dynamic Particle Swarm Optimization Algorithm Based on Chaotic Mutation


Abstract:

A novel dynamic particle swarm optimization algorithm based on chaotic mutation (DCPSO) is proposed to solve the problem of the premature and low precision of the common ...Show More

Abstract:

A novel dynamic particle swarm optimization algorithm based on chaotic mutation (DCPSO) is proposed to solve the problem of the premature and low precision of the common PSO. Combined with linear decreasing inertia weight, a kind of convergence factor is proposed based on the variance of the populationpsilas fitness in order to adjust ability of the local search and global search; The chaotic mutation operator is introduced to enhance the performance of the local search ability and to improve the search precision of the new algorithm. The experimental results show finally that the new algorithm is not only of greater advantage of convergence precision, but also of much faster convergent speed than those of common PSO (CPSO) and linear inertia weight PSO (LPSO).
Date of Conference: 23-25 January 2009
Date Added to IEEE Xplore: 02 February 2009
Print ISBN:978-0-7695-3543-2
Conference Location: Moscow, Russia

I. Introduction

Particle Swarm Optimization (PSO) is a population-based random search strategy and an adaptive optimization algorithm developed by Dr. Eberhart and Dr. Kennedy in 1995[1]. Because of the unique search mechanism, excellent convergence performance and easy realization, the algorithm has obtained rapid development and has been widely used in many fields since it was proposed [2] [3] [4].

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References

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