A simple strategy to maintain diversity and reduce crowding in particle swarm optimization

Date

2011

Authors

Chen, Stephen
Montgomery, James

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

Abstract

Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors -- updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad exploration of the search space with particles being drawn in many different directions. However, the population of attractors can converge quickly -- attractors can draw other particles towards them, and these particles can update their own personal bests to be near the first attractor. This convergence of attractors can be reduced by having a particle update the attractor it has approached rather than its own attractor/personal best. This simple change to the update procedure in particle swarm optimization incurs minimal computational cost, and it can lead to large performance improvements in multi-modal search spaces.

Description

Keywords

particle swarm optimization, crowding, niching, population diversity, multi-modal search spaces

Citation

Chen, S. & Montgomery, J. (2011). A simple strategy to maintain diversity and reduce crowding in particle swarm optimization. In D. Wang & M. Reynolds (Eds), AI 2011: Advances in artificial intelligence: Proceedings of the 24th Australasian Joint Conference in Artificial Intelligence, Perth, Australia, December 5-8, 2011 (pp. 281-290). Lecture Notes in Computer Science, 2011, Volume 7106/2011. Heidelberg: Springer

Source

Lecture Notes in Computer Science (LNCS)

Type

Conference paper

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DOI

10.1007/978-3-642-25832-9_29

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