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Improved NSGA-II algorithm for multi-objective scheduling problem in hybrid flow shop
Han ZH(韩忠华)1,2; Wang, Shiyao1; Dong XT(董晓婷)3; Ma, Xiaofu4
Department数字工厂研究室
Conference Name9th International Conference on Modelling, Identification and Control, ICMIC 2017
Conference DateJuly 10-12, 2017
Conference PlaceKunming, China
Source PublicationLecture Notes in Electrical Engineering
PublisherSpringer Verlag
Publication PlaceSingapore
2017
Pages273-289
Indexed ByEI
EI Accession number20181805112284
Contribution Rank1
ISSN1876-1100
ISBN978-981-10-7211-6
Keywordmulti-objective differential evolution hybrid flow shop
AbstractIn this paper, multi-objective optimization for hybrid flow shop scheduling problem is investigated. The delivery time penalty and the load imbalance penalty are taken as the evaluation metrics. We describe the optimization framework for this hybrid flow shop problem and design an improved NSGA-II algorithm for solution searching. Specifically, a multi-objective dynamic adaptive differential evolution algorithm (MODADE) is proposed to enhance the searching efficiency of the basic differential evolution operations. MODADE calculates the similarity between different individuals based on their Hamming distance, and dynamically generates the high-similarity individuals for the population. We further improve the MODADE algorithm by integrating the AP clustering mechanism. We compare the proposed algorithm and compare it with the state-of-the-art solutions. The numerical result shows that the proposed MODADE algorithm outperforms others in terms of the algorithm convergence, the number, and distribution of Pareto solutions.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22052
Collection数字工厂研究室
Corresponding AuthorHan ZH(韩忠华)
Affiliation1.Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China;
2.Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;
3.Sichuan College of Architectural Technology, Sichuan, China;
4.Virginia Tech, Blacksburg, VA, United States
Recommended Citation
GB/T 7714
Han ZH,Wang, Shiyao,Dong XT,et al. Improved NSGA-II algorithm for multi-objective scheduling problem in hybrid flow shop[C]. Singapore:Springer Verlag,2017:273-289.
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