改进的约束优化多目标遗传算法及工程应用

计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (3): 247-253.

改进的约束优化多目标遗传算法及工程应用

王俊年1，刘云连1，2，伍铁斌3

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

出版日期:2015-02-01 发布日期:2015-01-28

Improved constrained optimization multi-objective genetic algorithm and engineering applications

WANG Junnian1, LIU Yunlian1，2, WU Tiebin3

1.College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
2.Department of Information Science and Engineering, Hunan University of Humanities Science and Technology, Loudi, Hunan 417000, China
3.Department of Electrical and Mechanical Engineering, Hunan University of Humanities Science and Technology, Loudi, Hunan 417000, China

Online:2015-02-01 Published:2015-01-28

摘要/Abstract

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。

关键词: 多目标优化, 遗传算法, 约束优化问题

Abstract: 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.

Key words: multi-objective optimization, genetic algorithm, constrained optimization problem

王俊年1，刘云连1，2，伍铁斌3. 改进的约束优化多目标遗传算法及工程应用[J]. 计算机工程与应用, 2015, 51(3): 247-253.

WANG Junnian1, LIU Yunlian1，2, WU Tiebin3. Improved constrained optimization multi-objective genetic algorithm and engineering applications[J]. Computer Engineering and Applications, 2015, 51(3): 247-253.

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