协同量子智能体进化算法及其性能分析

Cooperative Quantum Agent Evolutionary Algorithm and Its Characteristic Analysis

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.