A Genetic Algorithm with Constrained Sorting Method for Constrained Optimization Problems

被引：3
|
作者：
Huang, Zhangjun [1 ]
Wang, Chengen [1 ]
Tian, Hong [2 ]
机构：
[1] Northeastern Univ, Key Lab Proc Ind Automat, Shenyang, Peoples R China
[2] Changsha Univ Sci & Technol, Sch Energy & Thermal Power Engn, Changsha, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年
关键词：
algorithm; constrained optimization; constraint handling; dynamic penalty; non-dominated sorting; constrained sorting; EVOLUTIONARY ALGORITHMS; DIFFERENTIAL EVOLUTION; DESIGN;
D O I：
10.1109/ICICISYS.2009.5358031
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Engineering problems are commonly optimization problems with various constraints For solving these constrained optimization problems, an effective genetic algorithm with a constrained sorting method is proposed in this work. The constrained sorting method is based on a dynamic penalty function and a non-dominated sorting technique that is used for ranking all the feasible and infeasible solutions in the whole evolutionary population The proposed algorithm is tested on five well-known benchmark functions and three engineering problems Experimental results and comparisons with previously reported results demonstrate the effectiveness, efficiency and robustness of the present algorithm for constrained optimization problems
引用
收藏
页码：806 / +
页数：2
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