Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Particle swarm optimization with resets: improving the balance between exploration and exploitation
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
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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.