具有混沌局部搜索策略的双种群遗传算法  

Dual population genetic algorithm with chaotic local search strategy

作　　者：谭跃[1,2] 谭冠政[1] 叶勇[2] 伍雪冬[3] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]湖南城市学院物电系,湖南益阳413000 [3]江苏科技大学电子与信息学院,江苏镇江212003

出　　处：《计算机应用研究》2011年第2期469-471,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(50275150);江苏省自然科学基金资助项目(BK2009727)

摘　　要：为提高遗传算法的局部和全局搜索能力,提出了一种具有混沌局部搜索策略的双种群遗传算法(CLS-DPGA)。CLSDPGA中,一个作为探测种群,另一个作为开发种群。两个种群按照不同交叉概率和变异概率进行进化,每个种群每进化一代后就对其最优解进行混沌局部搜索。若搜索到更优的解,则取代原最优解直至搜索到预设的混沌次数,同时两个种群之间每10代进行一次移民操作。六个Benchmark函数的实验结果证明,CLS-DPGA比另一种自适应局部搜索策略的遗传算法(a-hGA2)具有更好的寻优能力。This paper proposed dual population genetic algorithm with chaotic local search strategy（CLSDPGA） to improve local and global search ability of genetic algorithm.In CLSDPGA,one population was used as exploration population,the other was exploitation population.The two population was evolved by different crossover probability and mutation probability.At the end of each generation,applied chaotic local search to the optimal solution of each population,and the solution would be the new optimal solution if a solution found by chaotic local search was better than the optimal solution.Chaotic local search was not stopped until the predefined search time was elapsed.An immigration operation was down between the two population each ten generation.Experiment results on six benchmark functions show that CLSDPGA has the better ability of finding optimal solution than that of genetic algorithm with adaptive local search scheme（a-hGA2）.

关 键 词：混沌 局部搜索 双种群 遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]