基于柯西变异的多策略协同进化粒子群算法
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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.
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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[2]苏茂,王永骥,刘磊,等.基于改进粒子群算法的再入飞行器轨迹优化[J].计算技术与自动化,2011,30(4):55-59.SU Mao,WANG Yongji,LIU Lei,et al.Design of Reentry Vehicle Trajectory Optimization Based on Improved Particle Swarm Optimization Algorithm[J].Computer Technology and Automation,2011,30(4):55-59.
[3]闫斌斌,叶俊,闫杰.基于粒子群的高超声速飞行器模糊控制方法[J].科学技术与工程,2012,12(14):3493-3496.YAN Binbin,YE Jun,YAN Jie.Fuzzy Control Method of Hypersonic Vehicle Based on Particle Swarm Optimization[J].Science Technology and Engineering,2012,12(14):3493-3496.
[4]王路,邢清华,毛艺帆.再入高超声速滑翔飞行器轨迹快速优化[J].现代防御技术,2015,43(6):74-80.WANG Lu,XING Qinghua,MAO Yifan.Fast Trajectory Optimization for Reentry Hypersonic Glide Vehicle[J].MODERN DEFENCE TECHNOLOGY,2015,43(6):74-80.
[5]陈上上,何英姿,刘贺龙.基于粒子群优化的再入飞行器在线轨迹优化[J].上海航天,2015,32(6):1-7.CHEN Shangshang,HE Yingzi,LIU Helong.Onboard Trajectory Planning for Entry Vehicle Based on Particle Swarm Optimization[J].AEROSPACE SHANGHAI,2015,32(6):1-7.
[6]冯林爽,刘磊,王永骥.基于PSO和SQP混合优化算法的高超声速飞行器轨迹优化[C].第27届中国控制与决策会议,2015:4538-4542.Feng Linshuang,Liu Lei,Wang yongji.Trajectories optimization of hypersonic vehicle based on hybrid optimization algorithm of PSO and SQP[C]//Proceeding of 27th Chinese Control and Decision Conference(CCDC),2015:4538-4542.
[7]高立群,吴沛峰,邹德旋.基于变异策略的粒子群算法[J].东北大学学报(自然科学版),2010,31(11):1530-1533.GAO Liqun,WU Peifeng,ZOU Dexuan.Particle Swarm Optimization Based on Mutation Strategy[J].Iournal of Northeastern University(Natural Science),2010,31(11):1530-1533.
[8]康岚兰,董文永,田降森.一种自适应柯西变异的反向学习粒子群优化算法[J].计算机科学,2015,42(10):226-231.KANG Lanlan,DONG Wenyong,TIAN Jiangsen.Opposition-based Particle Swarm Optimization with Adaptive Cauchy Mutation[J].Computer Science,2015,42(10):226-231.
[9]吴润秀,孙辉,朱德刚,等.具有高斯扰动的局部引导粒子群优化算法[J].计算机工程与科学,2016,36(6):1183-1192.WU Runxiu,SUN Hui,ZHU Degang,et al.A particle swarm optimization algorithm based on local guidance and Ganss perturbation[J].Computer Engineering and Science,2016,36(6):1183-1192.
[10]徐冰纯,葛洪伟,王燕燕.基于多种群多模型协同进化的粒子群优化算法[J].计算机工程,2013,39(5):200-208.XU Bingchun,GE Hongwei,WANG Yanyan.Particle Swarm Optimization Algorithm Based on Multi-swarm and Multi-model Cooperative Evolution[J].Computer Engineering,2013,39(5):200-208.
[11]曾建潮,介婧,崔志华.微粒群算法[M].北京:科学出版社,2004:61-69.ZENG Chaojian,JIE Jing,CUI Zhihua.Particle Swarm Optimization[M].Bejing:Science Press,2004:61-69.
[12]X L Liu,G R Duan,K L Teo.Optimal soft landing control for moon lander[J].Automatica(S0005-1098),2008,44(4):1097-1103.
[13]刘凯,寇正.Open MP在并行计算中的应用[J].微型机与应用,2003,22(12):12-14.LIU Kai,KOU Zheng.Application of Open MP in parallel computing[J].Microcomputer and Application,2003,22(12):12-14.