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Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm
Su, Yang1,2; Jin, Saimeng1,2; Zhang, Xiangping3; Shen, Weifeng1,2; Eden, Mario R.4; Ren, Jingzheng5
2020-01-04
Source PublicationCOMPUTERS & CHEMICAL ENGINEERING
ISSN0098-1354
Volume132Pages:16
AbstractMulti-objective optimization (MOO) is frequently used to solve many practical problems of chemical processes but process designers only need a limited number of valuable solutions in the final results. In this study, an optimization strategy associated with an improved genetic algorithm was developed to search valuable solutions for stakeholders' preference more purposefully. The algorithm was improved to reduce overlapping solutions as a result of the discrete variables in practical problems, and it allowed users to set a reference point or an angle associated with a reference point to make solutions converge into the preferred spaces. Three test functions and two practical problems were used to highlight that the proposed strategy could make designers optimize processes more efficiently. Especially, the angle-based algorithm could be more effective than the distance-based one on the tri-objective problems. Thus, the developed strategy is robust in the optimization of processes assisted with the designer's preference. (C) 2019 Elsevier Ltd. All rights reserved.
KeywordMulti-objective optimization Preference Process optimization Genetic algorithm
DOI10.1016/j.compchemeng.2019.106618
Language英语
WOS KeywordEXTRACTIVE DISTILLATION ; OPTIMAL-DESIGN ; AZEOTROPES
Funding ProjectNational Natural Science Foundation of China[21878028] ; National Natural Science Foundation of China[21606026] ; Fundamental Research Funds for the Central Universities[2019CDQYHG021] ; Beijing Hundreds of Leading Talents Training Project of Science and Technology[Z17110 0001117154]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Chemical
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Beijing Hundreds of Leading Talents Training Project of Science and Technology
WOS IDWOS:000498396100020
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/38419
Collection中国科学院过程工程研究所
Corresponding AuthorShen, Weifeng
Affiliation1.Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China
2.Chongqing Univ, Natl Municipal Joint Engn Lab Chem Proc Intensifi, Chongqing 400044, Peoples R China
3.Chinese Acad Sci, Inst Proc Engn, Beijing Key Lab Ion Liquids Clean Proc, CAS Key Lab Green Proc & Engn, Beijing 100190, Peoples R China
4.Auburn Univ, Dept Chem Engn, Auburn, AL 36849 USA
5.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Su, Yang,Jin, Saimeng,Zhang, Xiangping,et al. Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm[J]. COMPUTERS & CHEMICAL ENGINEERING,2020,132:16.
APA Su, Yang,Jin, Saimeng,Zhang, Xiangping,Shen, Weifeng,Eden, Mario R.,&Ren, Jingzheng.(2020).Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm.COMPUTERS & CHEMICAL ENGINEERING,132,16.
MLA Su, Yang,et al."Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm".COMPUTERS & CHEMICAL ENGINEERING 132(2020):16.
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