Preserving diversity in particle swarm optimisation

Authors:
Tim Hendtlass
Affiliations:
Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, Melbourne, Australia
Venue:
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Year:
2003

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Abstract

Particle Swarm Optimisation (PSO) is an optimisation algorithm that shows promise. However its performance on complex problems with multiple minima falls short of that of the Ant Colony Optimisation (ACO) algorithm when both algorithms are applied to travelling salesperson type problems (TSP). Unlike ACO, PSO can be easily applied to a wider range of problems than TSP. This paper shows that by adding a memory capacity to each particle in a PSO algorithm performance can be significantly improved to a competitive level to ACO on the smaller TSP problems.