Multiobjective genetic algorithms applied to solve optimization problems

被引：87
|
作者：
Dias, AHF [1 ]
de Vasconcelos, JA
机构：
[1] Acesita Co, BR-180000 Timoteo, Minas Geras, Brazil
[2] Univ Fed Minas Gerais, Dept Elect Engn, BR-31270901 Belo Horizonte, MG, Brazil
来源：
IEEE TRANSACTIONS ON MAGNETICS | 2002年 / 38卷 / 02期
关键词：
electromagnetics; multiobjective evolutionary algorithms; nondominated sorting genetic algorithms;
D O I：
10.1109/20.996290
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we discuss multiobjective optimization problems solved by evolutionary algorithms. We present the nondominated sorting genetic algorithm (NSGA) to solve this class of problems and its performance is analyzed in comparing its results with those obtained with four others algorithms. Finally, the NSGA is applied to solve the TEAM benchmark problem 22 without considering the quench physical condition to map the Pareto-optimum front. The results in both analytical and electromagnetic problems show its effectiveness.
引用
收藏
页码：1133 / 1136
页数：4
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