Abstract
This paper points out that optimization methods should have robustness and adaptability to problems with different structure and adjustability of parameters provides adaptability. Particle swarm optimization, whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, several new parameters are introduced to particle swarm optimization on the basis of the Proximate Optimality Principle (POP). In this paper, we propose Adaptive Particle Swarm Optimization. And the effectiveness and feasibility of the proposed approach are demonstrated on simulations using typical nonconvex optimization problems.
