自适应混沌遗传混合算法及其参数敏感性分析

提出自适应搜索空间的混沌遗传混合算法.该方法不同于一般的混沌遗传混合算法,它在遗传进化的过程中根据群体多样性测度引入混沌算子,并从全局搜索空间以随机概率解析出优秀解域,对个体分两个区域进行混沌扰动:优秀解域细搜索和全局解域大扰动.数值仿真表明该算法既加快了收敛速度又提高了收敛精度,解决了传统遗传算法的早熟问题.

Self-Adapting Chaos-Genetic Hybrid Algorithm and Sensitivity Analysis of Its Parameters

Chaos optimization is good at searching local solutions and sensitive to initial value.A self-adapting chaos-genetic hybrid algorithm(SA-CGA) is thus proposed to combine the chaos optimization with TGA(typical genetic algorithm) together.Differing from other conventional chaos-genetic hybrid algorithms,in the algorithm proposed a chaos operator is introduced in the evolution process according to the measure of species diversity and an excellent solvability domain is given in global search space by means of random probability.Then,as a whole,two domains are chaotically disturbed,i.e.,searching in the excellent solvability domain in detail together with disturbing greatly the global solvability domain.Numerical simulation shows that the algorithm not only accelerates the convergence rate but improves its accuracy,thus solving the premature problem frequently found in TGA.