A Heterogeneous Particle Swarm
Almost all Particle Swarm Optimisation (PSO) algorithms use a number of identical, interchangeable particles that show the same behaviour throughout an optimisation. This paper describes a PSO algorithm in which the particles, while still identical, have two possible behaviours. Particles are not interchangeable as they make independent decisions when to change between the two possible behaviours. The difference between the two behaviours is that the attraction towards a particle’s personal best in one is changed in the other to repulsion from the personal best position. Results from experiments on three standard functions show that the introduction of repulsion enables the swarm to sequentially explore optima in problem space and enables it to outperform a conventional swarm with continuous attraction.
KeywordsParticle Swarm Optimisation Collective Intelligence Optimisation
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