Improving the non-dominate sorting genetic algorithm for multi-objective optimization

被引：8

作者：

Ghomsheh, V. Seydi

Khanehsar, M. Ahmadieh

Teshnehlab, M.

机构：

来源：

CIS WORKSHOPS 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY WORKSHOPS | 2007年

关键词：

D O I：

10.1109/CIS.Workshops.2007.192

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Non-dominate Sorting Genetic Algorithmic-II (NSGA-II) is a relatively recent technique for finding or approximating the Pareto-optimal set for multi-objective optimization problems. In different studies NSGA-II has shown good performance in comparison to other multi-objective evolutionary algorithms [10]. In this paper an improved version which is named Niching-NSGA-II (n-NSGA-II) is proposed. This algorithm uses new method after Non-dominate sorting procedure for keeping diversity. The comparison of n-NSGA-II with NSGA-II and other methods on ZDT test problems yields promising results.

引用

页码：89 / 92

页数：4

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