混合粗粒度遗传算法在约束最优化问题中的应用  

Application of Hybrid Coarse-grained Genetic Algorithm in Constrained Optimal Problems

作　　者：钱志勤[1] 王志鹏[2] 周炜[1] 

机构地区：[1]华东理工大学机械工程学院,上海200237 [2]中兴通讯移动事业部,上海201203

出　　处：《计算机工程》2004年第22期129-131,共3页Computer Engineering

摘　　要：选取粗粒度遗传算法,并针对其过早收敛、收敛速度慢的缺陷进行改进,提出混合粗粒度遗传算法。混合粗粒度遗传算法按照适应度函数值对染色体群体进行分组,各分组采用不同的惩罚系数、交叉、变异算子;同时采用同种互斥和最优解保留策略。实验结果表明该算法在约束最优化问题中应用良好。In this paper, a hybrid coarse-grained genetic algorithm(HCGGA) is proposed to solve the problems of premature convergence and the slow convergence rate of coarse-grained genetic algorithm(CGGA). All chromosomes are ranked according to their fitness values and divided into several subgroups in HCGGA. Each subgroup, whose values of punishment coefficient, crossover and mutation operators are different from other subgroups, operates independently. Otherwise the strategies of the same exclude and the best live are adopted. Experimental result shows that HCGGA is efficient in constrained optimal problems.

关 键 词：约束最优化问题 混合粗粒度遗传算法 目标函数 适应度函数 

分 类 号：O224[理学—运筹学与控制论] O242.23[理学—数学]