An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem

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	An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem
	Cao Y(曹阳)2,3,4,5,6,7 ; Shi HB(史海波)3,5,6
	2019
会议日期	June 3-5, 2019
会议地点	Nanchang, China
关键词	multi-objective flexible job shop scheduling problem artificial bee colony algorithm multiple subpopulation
页码	3822-3827
英文摘要	In 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.
产权排序	1
会议录	Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-7281-0105-7
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/25781]
专题	沈阳自动化研究所_数字工厂研究室
作者单位	1.Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China 2.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 5.110016, China 6.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 7.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
推荐引用方式 GB/T 7714	Cao Y,Shi HB. An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem[C]. 见:. Nanchang, China. June 3-5, 2019.