Cited By
View all- Zhou YWang JZhou YQiu ZBi ZCai Y(2016)Differential evolution with guiding archive for global numerical optimizationApplied Soft Computing10.1016/j.asoc.2016.02.01143:C(424-440)Online publication date: 1-Jun-2016
A dynamic archive based niching particle swarm optimizer using a small population size
Authors: 
Zhaolin Zhai, Xiaodong LiAuthors Info & Claims
Zhaolin Zhai, Xiaodong LiAuthors Info & ClaimsPages 83 - 90
Abstract
Many niching techniques have been proposed to solve multimodal optimization problems in the evolutionary computing community. However, these niching methods often depend on large population sizes to locate many more optima. This paper presents a particle swarm optimizer (PSO) niching algorithm only using a dynamic archive, without relying on a large population size to locate numerous optima. To do this, we record found optima in the dynamic archive, and allow particles in converged sub-swarms to be re-randomized to explore undiscovered parts of the search space during a run. This algorithm is compared with lbest PSOs with a ring topology (LPRT). Empirical results indicate that the proposed niching algorithm outperforms LPRT on several benchmark multimodal functions with large numbers of optima, when using a small population size.
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Australian Computer Society, Inc.
Australia
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Published: 17 January 2011
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ACSC '11 Paper Acceptance Rate 18 of 53 submissions, 34%;
Overall Acceptance Rate 136 of 379 submissions, 36%
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View all- Zhou YWang JZhou YQiu ZBi ZCai Y(2016)Differential evolution with guiding archive for global numerical optimizationApplied Soft Computing10.1016/j.asoc.2016.02.01143:C(424-440)Online publication date: 1-Jun-2016
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