改进的约束优化多目标遗传算法及工程应用  

Improved constrained optimization multi-objective genetic algorithm and engineering applications

作　　者：王俊年[1] 刘云连[1,2] 伍铁斌[3] 

机构地区：[1]湖南科技大学信息与电气工程学院,湖南湘潭411201 [2]湖南人文科技学院信息科学与工程系,湖南娄底417000 [3]湖南人文科技学院机电工程系,湖南娄底417000

出　　处：《计算机工程与应用》2015年第3期247-253,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60974048);湖南省高校创新平台开放基金项目(No.11K027);湖南省科技厅计划项目资助(No.2013FJ6073;No.2014GK3033);湖南省重点建设学科资助

摘　　要：利用多目标法处理约束条件,提出一种改进的基于多目标优化的遗传算法用于求解约束优化问题。该算法将约束优化问题转化为两个目标的多目标优化问题;利用庄家法构造非劣个体,将种群分为支配子种群和非支配子种群,以一定概率分别从支配子种群和非支配子种群中选择个体进行算术交叉操作,引导个体逐步向极值点靠近,增强算法的局部搜索能力,对非支配子种群进行多样性变异操作。8个标准测试函数和3个工程应用的仿真实验结果表明了该算法的有效性。Using multi-objective method to deal with constraint conditions, an improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, this algorithm is based on multi-objective technique,where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 8 benchmark functions and 3 engineering optimization problems, and compard with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search.

关 键 词：多目标优化 遗传算法 约束优化问题 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]