期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 55, 期 16, 页码 4765-4784出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2017.1292064
关键词
cloud manufacturing; combinatorial optimisation; Pareto optimisation; manufacturing service composition; artificial bee colony algorithm; quality of service
资金
- National Natural Science Foundation of China [51675186, 51175187]
- Science & Technology Foundation of Guangdong Province [2016B090918035, 2016A020228005]
This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters' impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.
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