Quality-aware multi-objective cloud manufacturing service composition optimization algorithm

被引：0
|
作者：
Liu G. [1 ]
Jia Z. [1 ,2 ]
机构：
[1] School of Computer Science and Technology, Anhui University, Hefei
[2] Key Lab of Intelligent Computing and Signal Processing, Anhui University, Ministry of Education, Hefei
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 02期
基金：
中国国家自然科学基金;
关键词：
evolutionary algorithm; multi-objective optimization; service composition; service quality;
D O I：
10.13196/j.cims.2021.0577
中图分类号：
学科分类号：
摘要：
To solve the difficult problem of weighing the weights of multiple targets in the cloud manufacturing service composition, as well as significantly improve the population diversity of the evolutionary algorithm in the solution process and effectively balances the global and local search capabilities of the evolutionary algorithm, an evolutionary algorithm based on adaptive selection and reverse learning strategy was proposed, while optimizing the time, cost, reliability, availability and credibility. To shorten the time to solve the combined solution, the K-mcans method was used to cluster the candidate services based on the quality of service, and the poorer services were eliminated. Then, the reverse learning strategy was used to improve the global search performance, and the global and local search capabilities of the algorithm were effectively balanced through selection and probability update strategics. The results of comparative experiments with four advanced algorithms showed that the proposed algorithm had better comprehensive performance. © 2024 CIMS. All rights reserved.
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收藏
页码：684 / 694
页数：10
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