A new genetic algorithm for global optimization of multimodal continuous functions

被引：62
|
作者：
Thakur, Manoj [1 ]
机构：
[1] Indian Inst Technol, Sch Basic Sci, Mandi 175001, India
来源：
JOURNAL OF COMPUTATIONAL SCIENCE | 2014年 / 5卷 / 02期
关键词：
Genetic algorithms; Global optimization; Pareto crossover; Power mutation; EVOLUTIONARY ALGORITHMS; CROSSOVER OPERATOR;
D O I：
10.1016/j.jocs.2013.05.005
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper a new genetic algorithm is developed to find the near global optimal solution of multimodal nonlinear optimization problems. The algorithm defined makes use of a real encoded crossover and mutation operator. The performance of GA is tested on a set of twenty-seven nonlinear global optimization test problems of variable difficulty level. Results are compared with some well established popular GAs existing in the literature. It is observed that the algorithm defined performs significantly better than the existing ones. (C) 2013 Elsevier B.V. All rights reserved.
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页码：298 / 311
页数：14
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