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

conference contribution
posted on 2023-05-23, 08:55 authored by Chen, S, James MontgomeryJames Montgomery
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.

History

Publication title

AI 2011: Advances in Artificial Intelligence

Editors

D Wang, M Reynolds

Pagination

281-290

ISBN

978-3-642-25831-2

Department/School

School of Information and Communication Technology

Publisher

Springer-Verlag

Place of publication

Berlin, Germany

Event title

24th Australasian Joint Conference on Artificial Intelligence

Event Venue

Perth, Australia

Date of Event (Start Date)

2011-12-05

Date of Event (End Date)

2011-12-08

Rights statement

Copyright 2011 Springer

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the information and computing sciences

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