Dynamic diversity enhancement in Particle Swarm Optimization (DDEPSO) algorithm for preventing from premature convergence

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Abstract

The problem of early convergence in the Particle Swarm Optimization (PSO) algorithm often causes the search process to be trapped in a local optimum. This problem often occurs when the diversity of the swarm decreases and the swarm cannot escape from a local optimal. In this paper, a novel dynamic diversity enhancement particle swarm optimization (DDEPSO) algorithm is introduced. In this variant of PSO, we periodically replace some of the swarm's particles by artificial ones, which are generated based on the history of the search process, in order to enhance the diversity of the swarm and promote the exploration ability of the algorithm. Afterwards, we update the velocity of the artificial particles in corresponding generating period by a new velocity equation with the minimum inertia weight in order to enhance the exploitation potentiality of the swarm. The performance of this approach has been tested on the set of twelve standard unimodal and multimodal (Rotated or unrotated) benchmark problems and the results have been compared with our previous work as well as four other variants of the PSO algorithm. The numerical results demonstrate that the proposed algorithm outperforms others in most of the test cases taken in this study.
Original languageEnglish
Pages (from-to)54-65
Number of pages12
JournalProcedia Computer Science
Volume24
DOIs
Publication statusPublished - 2013
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2013. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Particle Swarm Optimization (PSO) Algorithm
  • Population Diversity
  • Premature Convergence

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