Abstract
In particle swarm optimization (PSO), each particle searches for an optimal solution in such a way that its candidate solution becomes closer to the global best and its personal best. All the particles in the swarm tend to search in the neighborhood of the global best, which makes it difficult to apply PSO to optimization of multimodal objective functions. In order to search widely over the solution space, this paper presents PSO with enhanced autonomous search ability of each particle (PSO-ASA). PSO-ASA is based on the simple idea that the personal best of each particle is not updated if its current candidate solution is close to the global best. Furthermore, we propose a hybrid method combining PSO-ASA and the original PSO in order to be able to treat objective functions with various landscape features.
