Solving multiple-objective optimization problems using GISMOO algorithm

被引：1

作者：

Zinflou, Arnaud ^{[1
]}

Gagne, Caroline ^{[1
]}

Gravel, Marc ^{[1
]}

机构：

[1] Univ Quebec Chicoutimi, Dept Math & Informat, Chicoutimi, PQ, Canada

来源：

2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009) | 2009年

关键词：

evolutionnary algorithm; artificial immune systems; multiple-objective optimization; hybridization; Pareto front; MOKP;

D O I：

10.1109/NABIC.2009.5393704

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we proposed a new Pareto generic algorithm which hybridizes genetic algorithm and artificial immune systems. Numerical experiments were made using a classical benchmark in multiple-objective optimization (MOKP). Results show that our approach is able to obtain better performance than two state of the art approaches: NSGAII and PMSMO.

引用

页码：239 / 244

页数：6

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