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Multi-Swarms, Exclusion and Anti-Convergence in Dynamic Environments

Blackwell, Tim M. and Branke, Juergen. 2004. Multi-Swarms, Exclusion and Anti-Convergence in Dynamic Environments. IEEE Transactions on Evolutionary Computation, 10(4), pp. 459-472. ISSN 1089778X [Article]

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Abstract or Description

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we explore new variants of particle swarm optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to split the population of particles into a set of interacting swarms. These swarms interact locally by an exclusion parameter and globally through a new anti-convergence operator. In addition, each swarm maintains diversity either by using charged or quantum particles. This paper derives guidelines for setting the involved parameters and evaluates the multiswarm algorithms on a variety of instances of the multimodal dynamic moving peaks benchmark. Results are also compared with other PSO and evolutionary algorithm approaches from the literature, showing that the new multiswarm optimizer significantly outperforms previous approaches

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1109/TEVC.2005.857074

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
August 2004Published

Item ID:

993

Date Deposited:

12 Mar 2009 15:41

Last Modified:

20 Jun 2017 09:41

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/993

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