Memetic Differential Evolution for Constrained Numerical Optimization Problems

被引：0
|
作者：
Dominguez-Isidro, Saul [1 ]
Mezura-Montes, Efren [2 ]
Leguizamon, Guillermo [3 ]
机构：
[1] Natl Lab Adv Informat LANIA AC, Xalapa, Veracruz, Mexico
[2] Univ Veracruzana, Dept Artificial Intelligence, Xalapa, Veracruz, Mexico
[3] Univ Nacl San Luis, Dept Informat, San Luis, Argentina
来源：
2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2013年
关键词：
ALGORITHM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents a memetic algorithm for solving constrained numerical optimization problems. The proposed approach uses differential evolution as a global search algorithm, which was improved with a mathematical programming method called Powell's conjugate direction as a local search operator. To the best of the authors' knowledge, this is the first attempt to use such mathematical programming method within differential evolution for constrained optimization. The proposed algorithm was tested on 36 test problems used in the special session on "Single Objective Constrained Real-Parameter Optimization" in CEC'2010. The proposed algorithm is able to find competitive results with respect to the winner algorithm in that session.
引用
收藏
页码：2996 / 3003
页数：8
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