Chaos-enhanced accelerated particle swarm optimization

There are more than two dozen variants of particle swarm optimization (PSO) algorithms in the literature. Recently, a new variant, called accelerated PSO (APSO), shows some extra advantages in convergence for global search. In the present study, we will introduce chaos into the APSO in order to further enhance its global search ability. Firstly, detailed studies are carried out on benchmark problems with twelve different chaotic maps to find out the most efficient one. Then the chaotic APSO (CAPSO) will be compared with some other chaotic PSO algorithms presented in the literature. The performance of the CAPSO algorithm is also validated using three engineering problems. The results show that the CAPSO with an appropriate chaotic map can clearly outperform standard APSO, with very good performance in comparison with other algorithms and in application to a complex problem.

► Chaos-enhanced accelerated particle swarm optimization algorithms are presented.
► Twelve different chaotic maps are utilized to tune the main parameter of the accelerated particle swarm optimization.
► Chaotic accelerated particle swarm optimization algorithm outperforms the non-chaotic one.
► It has very good performance in comparison with other chaotic algorithms.
► To validate, a complex engineering problem is also solved.