多峰函数优化的混合遗传算法  

Hybrid Genetic Algorithms for Multimodal Optimization

作　　者：张琳[1] 郑忠[1] 高小强[2] 

机构地区：[1]重庆大学材料科学与工程学院 [2]重庆大学经济与工商管理学院,重庆400030

出　　处：《重庆大学学报（自然科学版）》2005年第7期51-54,共4页Journal of Chongqing University

摘　　要：研究了2种基于最速下降法和遗传算法的求解多峰函数优化问题的混合遗传算法,以Schaffer函数的全局优化问题和收敛概率、平均收敛时间和平均收敛值等评价指标检验了混合算法的性能.结果表明混合算法的性能优于单独的遗传算法或最速下降法,采用随机方式选择局部优化个体的混合遗传算法性能在总体上优于从每代群体中选择适应度高的个体进行局部优化的混合遗传算法.Hybrid genetic algorithms, which are based on steepest descent algorithm and genetic algorithm, are investigated for the purpose of multimodal optimization. The performances of the hybrid genetic algorithms are evaluated with criteria such as convergence probability, average convergence time and average convergence value of the function in the case of solving global optimization for Schaffer function. It is shown that the performances of the hybrid genetic algorithms are better than steepest decent algorithm or genetic algorithm, and the hybrid genetic algorithm, in which the individuals used for local optimization by steepest decent method are chosen by chance in each generation population, is more efficient than that in which the individuals used for local optimization by steepest descent method are selected from excellent individuals.

关 键 词：遗传算法 最速下降法 多峰函数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]