Chaos Driven PSO with Attractive Search Space Border Points

In this paper, a chaotic pseudorandom generator is used to enhance the performance of Particle Swarm Optimization with attractive search space borders. The original modification was proposed to prolong the exploration phase and improve the ability of the swarm to avoid local minima. The particles are randomly attracted towards the border points of the search space in each dimension. The well-known and frequently used CEC'13 Benchmark function set is used to test the performance of the proposed method and compare it to canonical PSO.