Mutative scale chaos particle swarm optimization algorithm based on self logical mapping function
基于逻辑自映射的变尺度混沌粒子群优化算法*

Aiming at the standard particle swarm optimization(PSO) existing shortcomings of premature and low convergence, this paper proposed a novel altorithm which used the method of mutative scale chaos optimization based on self logical mapping function(SLM-PSO). In SLM-PSO, computed a series of chaotic variables according to self logical mapping function at the end of each iteration, then the SLM-PSO algorithm attempted to search the better solutions around current best solutions by chaos optimization and shrink search field dynamically. The simulation results for benchmark functions suggest that the new proposed algorithm has better probability of finding the global optima and mean best values, especially for complex multimodal functions.