Hybrid particle swarm optimisation based on history information sharing

Authors:
Wenlong Fu;Mark Johnston;Mengjie Zhang
Affiliations:
Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

Particle Swarm Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In order to find a global optimum, some evolutionary search operators used in multi-agent genetic algorithms are integrated into a novel hybrid PSO, with the expectation of effectively escaping from local optima. Particles share their history information and then update their positions using the latest and best history information. Some benchmark high-dimensional functions (from 20 to 10000 dimensions) are used to test the performance of the hybrid algorithms. The results demonstrate that the algorithm can solve high-dimensional nonlinear optimisation problems and that the number of function evaluations required to do so increases with function dimension at a sublinear rate.