标题:A collaboration services scheduling method based on intelligent genetic algorithm
作者:Guo, Wei ;Xu, Meng ;Xu, Weixia ;Cui, Lizhen
通讯作者:Cui, Lizhen
作者机构:[Guo, W] School of Software, Shandong University, Jinan, 250100, China, Key Laboratory of Shandong Software Engineering, Jinan, 250100, China;[ Xu, M] 更多
会议名称:13th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2018
会议日期:18 August 2018 through 19 August 2018
来源:Communications in Computer and Information Science
出版年:2019
卷:917
页码:581-587
DOI:10.1007/978-981-13-3044-5_46
关键词:Collaboration services scheduling; Genetic algorithm; Multi-objective optimization; Self-adaption
摘要:The optimization problem of collaboration services scheduling is a major bottleneck restricting the efficiency and cost of collaboration services executing. Correct and efficient handling of scheduling problems contributes to reducing costs and increase efficiency. The traditional GA solves this multi-objective problem more comprehensively than the random algorithm such as stochastic greedy algorithm, but it still has some one-sidedness compared with the actual situation. This paper enhances the flexibility and diversity of the algorithm on the basis of traditional genetic algorithm. In the process of initial population selection, it adopts the method of determining the preliminary internal point internal modification, and optimizes the selection process in the iteration as the selection method based on population exchange to achieve the choice. Mutation factors in the variation based on the individual’s innate quality of adaptive selection enhance the diversity of the population. In the experiments, this algorithm can not only maintain individual diversity, increase the probability of excellent individuals, speed up the convergence rate, but also will not lead to the ultimate result of the local optimal solution. © Springer Nature Singapore Pte Ltd. 2019.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059061907&doi=10.1007%2f978-981-13-3044-5_46&partnerID=40&md5=855d36e80029dfae9a81dbd1175b470a
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