Improvement in Solution Search Performance of Deterministic PSO Using a Golden Angle

抄録

A particle swarm optimization (PSO) is one of the powerful systems for solving global optimization problems. The searching ability of such PSO depends on the inertia weight coefficient, and the acceleration coefficients. Since the acceleration coefficients are multiplied by a random vector, the system can be regarded as a stochastic system. In order to analyze the dynamics rigorously, we pay attention to a deterministic PSO, which does not contain any stochastic factors. On the other hand, the standard PSO may diverge depending on the random parameter. Because of this divergence property, the standard PSO has high performance compared with the deterministic PSO. Since the deterministic PSO does not have stochastic factors, the diversity of the particles of deterministic PSO is lost. Therefore its searching ability is worse. In order to give diversity to the deterministic PSO, the golden angle is applied to the rotation angle parameter of the deterministic PSO. We confirm the performance of the searching ability of the proposed PSO.