Exploring avoidance strategies and neighbourhood topologies in particle swarm optimisation

Abstract

Particle swarm optimisation (PSO) is a stochastic optimisation algorithm in which particles evaluate solutions in a problem space and converge on the best known solution. This paper presents a PSO variant with avoidance of worst locations (AWL). The particles in PSO AWL remember the worst previous positions as well as the best. This new information changes the motion of the particles and results in spending less time exploring areas which are known to have the worst fitness. A small influence from the worst locations leads to the best performance. The performance of PSO AWL is promising compared to the standard PSO. The PSO AWL also performs significantly better compared to previous implementations of worst location memory. This paper also explores the effect of static vs. dynamic topology on the PSO AWL. It is found that the dynamic topology, gradually increasing directed neighbourhoods (GIDN), greatly improves the performance of PSO AWL.