求解0-1动态优化问题的双概率原对偶遗传算法  

Double-probability primal-dual genetic algorithm for 0-1 dynamic optimization problems

作　　者：刘黎黎[1] 汪定伟[1] 王洪峰[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《系统工程学报》2009年第5期636-640,共5页Journal of Systems Engineering

基　　金：国家自然科学基金资助项目(70931001;70771021);创新群体资助项目(60821063);国家教育部博士点基金资助项目(200801450008)

摘　　要：在原对偶遗传算法(PDGA)的基础上,提出一种双概率原对偶遗传算法(DPPDGA).引入弱势基因位值与强势基因位值的概念,对二者赋予不同的对偶映射概率,并对两个对偶概率进行适应性调整.比较原始算法,改进算法使种群具有更理想的多样性,并利于种群较快地收敛到满意解.仿真结果表明,该算法在0-1动态优化问题的求解中具有更好的性能.On the basis of primal-dual genetic algorithm （PDGA）, a new double-probability-based primal-dual（DPPDGA） genetic algorithm is proposed for 0-1 dynamic optimization problems. The concepts of inferior-allele and superior-allele are introduced, which are given different probabilities respectively for dual mapping, and they are adjusted adaptively. The algorithm has improvement on keeping diversity and guaranteeing convergence rate of population. The numerical experiment results show that the proposed algorithm has better performance for solving 0-1 dynamic optimization problems than that of PDGA.

关 键 词：遗传算法 动态优化 双概率 原对偶映射 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]