There are some adjustable parameters which directly influence the performance and stability of Particle Swarm Optimization algorithm. In this paper, stabilities of PSO with constant parameters and time-varying paramet...There are some adjustable parameters which directly influence the performance and stability of Particle Swarm Optimization algorithm. In this paper, stabilities of PSO with constant parameters and time-varying parameters are analyzed without Lipschitz constraint. Necessary and sufficient stability conditions for acceleration factor P and inertia weight w are presented. Experiments on benchmark functions show the good performance of PSO satisfying the stability condition, even without Lipschitz constraint. And the inertia weight ω value is enhanced to (-1,1). Keywords Lipschitz constraint - Time-varying discrete system - Adaptive acceleration factor - Stability展开更多
文摘There are some adjustable parameters which directly influence the performance and stability of Particle Swarm Optimization algorithm. In this paper, stabilities of PSO with constant parameters and time-varying parameters are analyzed without Lipschitz constraint. Necessary and sufficient stability conditions for acceleration factor P and inertia weight w are presented. Experiments on benchmark functions show the good performance of PSO satisfying the stability condition, even without Lipschitz constraint. And the inertia weight ω value is enhanced to (-1,1). Keywords Lipschitz constraint - Time-varying discrete system - Adaptive acceleration factor - Stability
