Abstract
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. This paper investigates the adaptive Particle Swarm Optimization from the viewpoint of search history. Some numerical simulations were carried out in order to examine the adaptability of the proposed approach.
