Self-Adaptive Particle Swarm Optimization

摘要

Particle swarm optimization (PSO) has been used to solve a wide variety of optimization problems. The basic PSO algorithm contains a number of control parameters, including the inertia weight, w, and the acceleration coefficients, c_1 and c_2. The PSO, as an optimization algorithm, is ideally suited to optimize its own parameters. This paper proposes that the control parameters of PSO be optimized in a secondary swarm where each position vector component of each particle contains a prospective PSO control parameter (i.e. w, c_1 and c_2) of the main swarm. This approach relieves the user from specifying appropriate parameters when using PSO. Application of the self-adaptive particle swarm optimizer (SAPSO) to 12 well known test functions shows that SAPSO managed to reach pre-specified values quicker than an adaptive PSO using fitness rank to update the inertia weight.