An improved genetic algorithm for multi-objective optimization

被引：0
|
作者：
Lin, F [1 ]
He, GM [1 ]
机构：
[1] Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China
来源：
PDCAT 2005: Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Proceedings | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The article points out that the traditional methods for multi-objective optimization exist some drawbacks, and presents a new method for multi-objective optimization: Combining genetic search with local search. The improved genetic algorithm (IGA) introduces local search as a means of acceleration and refinement of the solutions of genetic search. The experiments show that the improved genetic algorithm (IGA), compared with the traditional genetic algorithm (GA), can improve efficiency of optimization and ensure a better convergence to the true Pareto optimal front.
引用
收藏
页码：938 / 940
页数：3
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