Robust adaptive particle swarm optimization

It is well known that particle swarm optimization (PSO), which was originally proposed by J. Kennedy et al., is a powerful algorithm for solving unconstrained and constrained global optimization problems. Appropriate adjustment of its parameters, however, requires a lot of time and labor when PSO is applied to real optimization problems. In this paper, we point out that giving diversity to the parameter of each particle enables PSO to solve various problems. And we propose the algorithm that has diversity of particles and an adaptive strategy for tuning the parameters. Some numerical simulations were carried out in order to examine the search ability of the proposed approach.