关键词: 嵌入式粒子群算法, 混沌, 自适应, 帐篷映射, 平均粒径, 适应度方差

Abstract: In order to prevent appearing premature convergence in searching iterations of particle swarm optimization algorithm, an adaptive chaotic embedded particle swarm optimization algorithm is proposed. It embeds the chaos variables into the standard PSO algorithm（SPSO） and adjusts parameters adaptively. In this algorithm, to take full advantage of the randomicity, ergodicity and disciplinarian of chaos, Tent chaotic maps is used to substitute the random numbers of the SPSO; the inertia weight and acceleration coefficient is adjusted adaptively with nonlinearly strategy; the population fitness variance of particle swarm and average distance amongst points are put forward to estimate particles whether being focusing or discrete, then chaotic researching is applied to jump out of local optimum. Simulation experimental results show this algorithm has very good global optimization ability, high precision and robustness.

Key words: embedded particle swarm optimization algorithm, chaos, adaptive, tent mapping, average distance amongst points, fitness variance