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An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem
Cao Y(曹阳)1,2,3,4,5,6; Shi HB(史海波)2,3,5
Department数字工厂研究室
Conference Name31st Chinese Control and Decision Conference, CCDC 2019
Conference DateJune 3-5, 2019
Conference PlaceNanchang, China
Source PublicationProceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages3822-3827
Indexed ByEI
EI Accession number20194207533600
Contribution Rank1
ISBN978-1-7281-0105-7
Keywordmulti-objective flexible job shop scheduling problem artificial bee colony algorithm multiple subpopulation
AbstractIn this paper, we propose a novel artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In this algorithm, the whole population is divided into multiple subpopulations at each generation, and the size of each subpopulation is adaptively adjusted based on the information derived from its search results. Furthermore, the two mutation strategies implemented in the differential evolution algorithm are embedded in the proposed algorithm to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively. Experimental results on the well-known benchmark multi-objective problems show that the improvements of the strategies are positive and that the proposed algorithm is better than or at least competitive to some previous multi-objective evolutionary algorithms.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25781
Collection数字工厂研究室
Affiliation1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang
3.110016, China
4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
5.University of Chinese Academy of Sciences, Beijing 100049, China
6.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
7.Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
Recommended Citation
GB/T 7714
Cao Y,Shi HB. An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem[C]. New York:IEEE,2019:3822-3827.
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