A Variable Neighborhood Particle Swarm Algorithm Based on the Visual of Artificial Fish

ABSTRACT

Inspired by the model of artificial fish school algorithm, a novel particle swarm optimizer with the increasing visual of artificial fish is presented after introducing the visual of artificial fish into particle swarm optimization. The neighborhood of each particle is dynamically changed through continually expand the visual of each particle. The local optimal strategy and the global optimal strategy are combined to enhance information sharing among particles. By testing six standard benchmark functions, experimental results show that the improved algorithm performs better than the traditional PSO and may avoid falling into the local optimum instead.

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