This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle Swarm Optimization (PSO), 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, a new parameter is introduced into 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 the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems.