Sample particle swarm optimization and its dynamic behavior
采样粒子群优化模型及其动力学行为分析

A new sample particle swarm optimization model (SPSO) with variable sample time is proposed. The stability of the optimization behavior is analyzed by applying Lyapunov function to the error dynamic system. The further analysis of particle trajectory gives the bound of sample time. The convergence theory shows that SPSO is not a local optimizer. For multimode function optimization, a quantum SPSO(Q-SPSO) is provided to deal with multiple local optima. The experiment with different sample time investigates the influence on optimization behavior, demonstrating the advantages of SPSO. The tests of Q-SPSO on multimode optimization function show the efficacy of the algorithm.