种群分布式并行遗传算法解化工多目标优化问题

doi:10.16085/j.issn.1000-6613.2015.05.009

化工进展 ›› 2015, Vol. 34 ›› Issue (05): 1236-1240.DOI: 10.16085/j.issn.1000-6613.2015.05.009

种群分布式并行遗传算法解化工多目标优化问题

潘欣, 刘海燕, 廖安, 鄢烈祥, 史彬

武汉理工大学化学化工与生命科学学院, 湖北武汉 430070

收稿日期:2014-09-26 修回日期:2014-11-18 出版日期:2015-05-05 发布日期:2015-05-05
通讯作者: 鄢烈祥,教授.E-mailhgxtgc@163.com.
作者简介:潘欣(1989—),男,硕士研究生.
基金资助:
国家自然科学基金(21376185)及国家高技术研究发展计划项目(2011AA02A206).

Solving multi-objective optimization in chemical engineering by using populations distributed parallel genetic algorithm

PAN Xin, LIU Haiyan, LIAO An, YAN Liexiang, SHI Bin

School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, Wuhan 430070, Hubei, China

Received:2014-09-26 Revised:2014-11-18 Online:2015-05-05 Published:2015-05-05

摘要/Abstract

摘要： 带精英策略的非支配排序遗传算法(NSGA-II)在与流程模拟软件Aspen Plus结合求解化工多目标优化问题方面耗时较高.为了解决这一问题,本文提出了一种种群分布式的并行遗传算法(populations distributed parallel genetic algorithm,PDPGA),将模拟计算任务分配给局域网的多台子节点计算机并行执行.以氯乙烯精制的多目标优化过程为研究对象,选取氯乙烯采出量最大化和系统总能耗最小化为两个目标,低沸塔和高沸塔的质量回流比、塔顶馏出率和塔压6个操作参数为优化变量.分别应用PDPGA和NSGA-II对上述过程进行优化求解,二者的种群规模均设为70,进化代数均设为70,PDPGA使用1主节点和2子节点共3台计算机.结果表明,与直接应用NSGA-II进行串行优化相比,PDPGA优化方法能充分利用闲置的计算机资源、有效提高解得质量和大幅降低优化计算的时间.

关键词: 算法, 多目标, 优化, 模拟, 并行计算

Abstract: The optimizing method of non dominated sorting genetic algorithm with elitist strategy(NSGA-Ⅱ)combined with process simulation software Aspen Plus has high time consumption in solving multi-objective optimization in chemical engineering. This paper presented a populations distributed parallel genetic algorithm(PDPGA),and assigned the simulated computation tasks to multiple computers in local area network(LAN),in order to optimize a vinyl chloride distillation process. The two optimization objects were the maximum production of vinyl chloride and the minimum total energy,and the six optimization parameters were mass reflux ratio,distillation rate and pressure of the low boiling tower and high boiling tower. The PDPGA and NSGA-Ⅱ were used to solve the problem. The populations and generations of both systems were 70. The results showed that PDPGA was able to fully use idle computers and improve optimizing efficiency and the quality of solutions.

Key words: algorithm, multi-objective, optimization, simulation, parallel computing

中图分类号:

TQ021.8

潘欣, 刘海燕, 廖安, 鄢烈祥, 史彬. 种群分布式并行遗传算法解化工多目标优化问题[J]. 化工进展, 2015, 34(05): 1236-1240.

PAN Xin, LIU Haiyan, LIAO An, YAN Liexiang, SHI Bin. Solving multi-objective optimization in chemical engineering by using populations distributed parallel genetic algorithm[J]. Chemical Industry and Engineering Progree, 2015, 34(05): 1236-1240.

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种群分布式并行遗传算法解化工多目标优化问题

Solving multi-objective optimization in chemical engineering by using populations distributed parallel genetic algorithm

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