摘要
为了提高粒子群算法在优化仿真中的性能,提出基于柯西变异的多策略协同进化粒子群算法。该方法将种群划分为3个子群,以一定的概率选中粒子进行柯西变异,未被选中的粒子采用不同的子群进化策略(大范围搜索策略,精细搜索策略,自适应速度更新策略)来调节自身的开发能力和探测能力,子群间通过共享信息来达成协同合作。仿真中将3种策略应用在3种测试函数的优化中,验证3种策略的优点,同时对月球软着陆进行仿真寻优,结果表明改进PSO的优化性能优于其它改进算法,采用OpenMP对改进PSO进行并行化仿真,提高并行化PSO算法的效率。
For improving the performance of particle swarm optimization(PSO) in optimization simulation, a multi-strategy cooperative evolutionary PSO based on Cauchy mutation strategy is proposed. The new algorithm divides the whole swarm into three sub-swarms. A part of particles is selected to Cauchy mutation with a certain probability, and the rest of particles adjust their exploitation and exploration by different evolutionary strategies(large-scale search strategy, local search strategy, and adaptive velocity updating strategy). The sub-swarms share their information to achieve cooperation. Three strategies are used to optimize three test functions, and the result shows the advantages of three strategies. The simulation experiment uses the soft lunar landing problem as the simulation model to optimize the trajectory. Simulation results indicate that the performance of improved PSO is superior to other PSO. The simulation uses Open MP to parallelization optimization, which improves the efficiency of the algorithm.
引文
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