An improved non-dominated sorting genetic algorithm for multi-objective optimization based on crowding distance

被引：0
|
作者：
机构：
[1] Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai
来源：
Xia, Tian-Liang (xiatianliang123@126.com) | 1600年 / Springer Verlag卷 / 462期
关键词：
Crowding distance; Elite preservation; Genetic algorithm; Multi-objective optimization;
D O I：
10.1007/978-3-662-45261-5_8
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
An improved non-dominated sorting genetic algorithm (INSGA) is introduced for multi-objective optimization. In order to keep the diversity of the population, a modified elite preservation strategy is adopted and the evaluation of solutions’ crowding degree is integrated in crossover operations during the evolution. The INSGA is compared with the NSGA-II and other algorithms by applications to five classical test functions and an environmental/economic dispatch (EED) problem in power systems. It is shown that the Pareto solution obtained by INSGA has a good convergence and diversity. © Springer-Verlag Berlin Heidelberg 2014.
引用
收藏
页码：66 / 76
页数：10
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