协同量子智能体进化算法及其性能分析
|
摘要
针对当前量子进化算法的特点和不足,提出了一种分层协同进化的量子智能体进化算法.将种群个体视为以量子编码的智能体,采取三级进化方法,在子种群之间进行个体交流,子种群内部进行个体竞争操作,个体内部能够进行局部调整,使得进化操作能够作用在不同的小生境范围内,增强了进化的粒度.利用不动点定理对所提算法的收敛性进行分析,结果显示,算法能够收敛到最优值.对多个基准函数进行仿真对比分析,该算法具有更好的收敛精度.
Aiming at the drawback for the quantum optimization algorithm,a novel cooperative quantum agent optimization algorithm is proposed. The individual in the population can be viewed as the agent using quantum bit code,and the evolutionary process can be divided into three phases. The information and individual can exchange between subpopulation,the individual can also compete with each other and adjust slightly. The evolutionary can carry through in the different niche,so it can enhance the evolutionary granularity. The trait of convergence is analyzed in view of the functional analysis. The fixed point theorem is used to prove the convergence of the algorithm,and the theorem shows that the proposed algorithm can reach the satisfactory solution set. Simulation results of benchmark function demonstrate that the algorithm performs well than other algorithms,and can get better solution.
Aiming at the drawback for the quantum optimization algorithm,a novel cooperative quantum agent optimization algorithm is proposed. The individual in the population can be viewed as the agent using quantum bit code,and the evolutionary process can be divided into three phases. The information and individual can exchange between subpopulation,the individual can also compete with each other and adjust slightly. The evolutionary can carry through in the different niche,so it can enhance the evolutionary granularity. The trait of convergence is analyzed in view of the functional analysis. The fixed point theorem is used to prove the convergence of the algorithm,and the theorem shows that the proposed algorithm can reach the satisfactory solution set. Simulation results of benchmark function demonstrate that the algorithm performs well than other algorithms,and can get better solution.
引文
[1]Han K H,Kim J H.Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[J].IEEETransaction on Evolutionary Computation,2002,6(6):580-593.
[2]Nezamabadi-Pour H.A quantum-inspired gravitational search algorithm for binary encoded optimization problems[J].Engineering Applications of Artificial Intelligence,2015,40(1):62-75.
[3]Liu Tianyu,Jiao Licheng,Ma Wenping,et al.Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch[J].Applied Soft Computing,2016,48(1):597-611.
[4]Yuan Xiaohui,Wang Pengtao,Yuan Yanbin,et al.Anew quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem[J].Energy Conversion and Management,2015,100(1):1-9.
[5]Zhong Weicai,Liu Jing,Xue Mingzhi,et al.A multiagent genetic algorithm for global numerical optimization[J].IEEE Transactions on System,Man,and Cybernetics-Part B:Cybernetics,2004,34(2):1128-1141.
[6]潘晓英,焦李成,刘芳.求解SAT问题的多智能体社会进化算法[J].计算机学报,2014,37(9):2011-2020.Pan Xiaoying,Jiao Licheng,Liu Fang.A multi-agent social evolutionary algorithm for SAT problem[J].Chinese Journals of Computers,2014,37(9):2011-2020.
[7]Wang Shengyao,Wang Ling.A knowledge-based multiagent evolutionary algorithm for semiconductor final testing scheduling problem[J].Knowledge-Based Systems,2015,84(1):1-9.
[8]吴亚丽,薛芬.知识引导的多目标多智能体进化算法[J].控制理论与应用,2014,31(8):1069-1076.Wu Yali,Xue Fen.Knowledge-guided multi-objective multi-agent evolutionary algorithm[J].Control Theory and Applications,2014,31(8):1069-1076.
[9]Zheng Xiaolong,Wang Ling.A multi-agent optimization algorithm for resource constrained project scheduling problem[J].Expert Systems with Applications,2015,42(15-16):6039-6049.
[10]刘振,胡云安.协同进化免疫记忆克隆算法[J].四川大学学报(工程科学版),2013,45(1):138-145.Liu Zhen,Hu Yun'an.Coevoluationary immune memory clonal algorithm[J].Journal of Sichuan University(Engineering Science Edition),2013,45(1):138-145.
[11]毕晓君,张永建,沈继红.高维多目标多方向协同进化算法[J].控制与决策,2014,29(10):1737-1743.Bi Xaiojun,Zhang Yongjian,Shen Jihong.High-dimensional multi-objective multi-directional co-evolutionary algorithm[J].Control and Decision,2014,29(10):1737-1743.
[12]Fang Min,Groen F C A.Collaborative multi-agent reinforcement learning based on experience propagation[J].Journal of Systems Engineering and Electronics,2013,24(4):683-689.
[13]李阳阳,焦李成.求解SAT问题的量子免疫克隆算法[J].计算机学报,2007,30(2):176-183.Li Yangyang,Jiao Licheng.Quantum-inspired immune clonal algorithm for SAT problem[J].Chinese Journal of Computers,2007,30(2):176-183.
[14]刘振,胡云安.协同进化扩展紧致量子进化算法[J].控制与决策,2014,29(2):320-326.Liu Zhen,Hu Yun'an.Coevolutionary quantum evolutionary algorithm based on extended compact algorithm[J].Control and Decision,2014,29(2):320-326.
[15]胡适耕.应用泛函分析[M].北京:科学出版社,2012:47-62.
[16]Pedro G G,Carlos A,Lario F C.An agent-based genetic algorithm for hybrid flowshops with sequencedependent setup times to minimise makespan[J].Expert Systems with Applications,2012,39(9):8095-810.
[2]Nezamabadi-Pour H.A quantum-inspired gravitational search algorithm for binary encoded optimization problems[J].Engineering Applications of Artificial Intelligence,2015,40(1):62-75.
[3]Liu Tianyu,Jiao Licheng,Ma Wenping,et al.Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch[J].Applied Soft Computing,2016,48(1):597-611.
[4]Yuan Xiaohui,Wang Pengtao,Yuan Yanbin,et al.Anew quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem[J].Energy Conversion and Management,2015,100(1):1-9.
[5]Zhong Weicai,Liu Jing,Xue Mingzhi,et al.A multiagent genetic algorithm for global numerical optimization[J].IEEE Transactions on System,Man,and Cybernetics-Part B:Cybernetics,2004,34(2):1128-1141.
[6]潘晓英,焦李成,刘芳.求解SAT问题的多智能体社会进化算法[J].计算机学报,2014,37(9):2011-2020.Pan Xiaoying,Jiao Licheng,Liu Fang.A multi-agent social evolutionary algorithm for SAT problem[J].Chinese Journals of Computers,2014,37(9):2011-2020.
[7]Wang Shengyao,Wang Ling.A knowledge-based multiagent evolutionary algorithm for semiconductor final testing scheduling problem[J].Knowledge-Based Systems,2015,84(1):1-9.
[8]吴亚丽,薛芬.知识引导的多目标多智能体进化算法[J].控制理论与应用,2014,31(8):1069-1076.Wu Yali,Xue Fen.Knowledge-guided multi-objective multi-agent evolutionary algorithm[J].Control Theory and Applications,2014,31(8):1069-1076.
[9]Zheng Xiaolong,Wang Ling.A multi-agent optimization algorithm for resource constrained project scheduling problem[J].Expert Systems with Applications,2015,42(15-16):6039-6049.
[10]刘振,胡云安.协同进化免疫记忆克隆算法[J].四川大学学报(工程科学版),2013,45(1):138-145.Liu Zhen,Hu Yun'an.Coevoluationary immune memory clonal algorithm[J].Journal of Sichuan University(Engineering Science Edition),2013,45(1):138-145.
[11]毕晓君,张永建,沈继红.高维多目标多方向协同进化算法[J].控制与决策,2014,29(10):1737-1743.Bi Xaiojun,Zhang Yongjian,Shen Jihong.High-dimensional multi-objective multi-directional co-evolutionary algorithm[J].Control and Decision,2014,29(10):1737-1743.
[12]Fang Min,Groen F C A.Collaborative multi-agent reinforcement learning based on experience propagation[J].Journal of Systems Engineering and Electronics,2013,24(4):683-689.
[13]李阳阳,焦李成.求解SAT问题的量子免疫克隆算法[J].计算机学报,2007,30(2):176-183.Li Yangyang,Jiao Licheng.Quantum-inspired immune clonal algorithm for SAT problem[J].Chinese Journal of Computers,2007,30(2):176-183.
[14]刘振,胡云安.协同进化扩展紧致量子进化算法[J].控制与决策,2014,29(2):320-326.Liu Zhen,Hu Yun'an.Coevolutionary quantum evolutionary algorithm based on extended compact algorithm[J].Control and Decision,2014,29(2):320-326.
[15]胡适耕.应用泛函分析[M].北京:科学出版社,2012:47-62.
[16]Pedro G G,Carlos A,Lario F C.An agent-based genetic algorithm for hybrid flowshops with sequencedependent setup times to minimise makespan[J].Expert Systems with Applications,2012,39(9):8095-810.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |