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中文摘要：
为了发挥粒子群算法和专用遗传算法的各自优点,提出了一种将二者结合的切换优化策略.该策略前期采用一种基于种群最优个体混沌化的混沌粒子群算法,后期选用专用遗传算法.通过大量仿真实验确定了在迭代代数、种群标准差和最优个体适应度差三种切换指标下各自的最优切换条件.与单一专用遗传算法和单一混沌粒子群算法的仿真对比表明：本文提出的切换优化策略在综合路径长度、平滑性和规划时间三个性能指标后具有一定的优越性.
英文摘要：
A switching strategy based on （CPSO-SGA） was presented by combining their chaos particle swarm optimization own advantages. In the switching and specialized genetic algorithm strategy, CPSO is applied in the former step and SGA is executed in the later step. The best switching conditions under three switching indices of iteration steps, population standard deviation, and optimal individual fitness values were determined by large amounts of simulation experiments. In comparison with single SGA and single CPS0, the proposed switching strategy CPSO-SGA has a better performance when path length, smoothness, and running time are taken into consideration.
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