摘要
为了提高差分进化算法的全局搜索能力和收敛速率,本文提出了一种双群体伪并行差分进化算法.该算法结合差分进化算法DE/best/2/bin变异方式局部搜索能力强、收敛速度快,和DE/rand/1/bin变异方式全局搜索能力强、鲁棒性好的特点,采用串行算法结构实现并行差分进化算法独立进化、信息交换的思想.为使初始化个体均匀分布在搜索空间,提高算法收敛到全局最优解的鲁棒性,提出了一种基于平均熵的初始化策略.典型Benchmarks函数测试和非线性系统模型参数估计结果表明,该方法能显著提高算法的收敛速率和全局搜索能力.
To improve the global searching ability and convergence speed of differential evolution algorithm （DE）, a pseudo parallel differential evolution algorithm with dual subpopulations （DSPPDE） is proposed in this paper. Combining with the properties of good local searching ability and fast convergence speed of DE/best/2/bin mutation scheme and the properties of good global searching ability and robustness of DE/rand/l/bin mutation scheme, the algorithm employs the ideal of isolated evolution and information exchanging in parallel DE algorithm by serial program structure. To diversify the initial individuals in the search space ,and improve the robustness of convergence to the global optimum, an initialization tactic based on the mean entropy is proposed. The tests of several classic Benchmarks functions and the parameters estimation result of a nonlinear system model show that the proposed algorithm can improve the convergence speed and the global searching ability greatly.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2007年第3期453-458,共6页
Control Theory & Applications
基金
国家自然科学基金资助项目(60375001)
高校博士点基金资助项目(20030532004).
关键词
双群体
差分进化算法
平均熵
参数估计
dual subpopulations
DE algorithm
mean entropy
parameters estimation
