A Parallel Genetic Algorithm in Multi-objective Optimization

被引：8

作者：

Wang Zhi-xin ^{[1
]}

Ju Gang ^{[1
]}

机构：

[1] Southeast Univ, Sch Energy & Environm, Nanjing 210096, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

Multi-objective optimization; NSGA-II; Parallel genetic algorithm; Individual migration; Individual update;

D O I：

10.1109/CCDC.2009.5192490

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Based on the combination of NSGA-II algorithm and parallel genetic algorithm, this paper presents a parallel genetic algorithm for multi-objective optimization (PNSGA). At the evolving process of this new algorithm, an individual migration to improve the parallel searching speed is applied to improve the efficiency of this algorithm and the accuracy of Pareto optimal set; at the same time, an individual update strategy is introduced to keep the diversity of Pareto optimal set. Data show that the Pareto optimal solutions or the solution candidates output by PNSGA that are scattered extensively and uniformly.

引用

页码：3497 / 3501

页数：5

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