Development of Canonical Particle Swarm Optimization based on Dynamical System Theory

We developed a nonlinear mapping optimization method with invariance based on analysis result of invariance of solution search performance due to variable dependency and ill-scale. Although the proposed algorithm is a deterministic system, local search capability improves and it has invariance. Based on the preliminary simulation results, we confirmed that the nonlinear mapping optimization method has higher search performance than the particle swarm optimization. When the proposed algorithm applies to machine learning, we confirmed that efficient learning result is obtained. The PWM inverter that is designed by using the proposed optimization method exhibits high power conversion efficiency.