Multi-objective optimization based on parallel multi-families genetic algorithm

被引：0

作者：

Lu, Hai ^{[1
]}

Yan, Liexiang ^{[1
]}

Shi, Bin ^{[1
]}

Lin, Zixiong ^{[1
]}

Li, Xiaochun ^{[2
]}

机构：

[1] College of Chemical Engineering, Wuhan University of Technology, Wuhan 430070, Hubei

[2] Zhenjiang Yctionsoft Co. Ltd., Zhenjiang 212009, Jiangsu

来源：

Huagong Xuebao/CIESC Journal | 2012年 / 63卷 / 12期

关键词：

Multi-objective genetic algorithm; Optimization; Parallel computing; Process simulator;

D O I：

10.3969/j.issn.0438-1157.2012.12.035

中图分类号：

学科分类号：

摘要：

A parallel multi-families genetic algorithm (PMOGA) is proposed to reduce computing burden which is incurred in the solution of the multi-objective optimization problem in chemical process when combining the single genetic algorithm (GA) with the process simulator. A master-slave node distributed computing strategy is employed in the proposed algorithm. Based on the idea of decomposition-coordination, the Pareto curve is divided into multi-sections, and then the calculation task of each sub-section is assigned to single computer in LAN to reduce the computing time. The proposed method has been tested on two practical chemical examples. The results show that PMOGA is superior to single GA in both uniformity and comprehensiveness of the Pareto solutions. © All Rights Reserved.

引用

页码：3985 / 3990

页数：5

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