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- W2588975087 abstract "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 paramet..." @default.
- W2588975087 created "2017-02-24" @default.
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- W2588975087 date "2017-02-15" @default.
- W2588975087 modified "2023-10-13" @default.
- W2588975087 title "A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition" @default.
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- W2588975087 doi "https://doi.org/10.1080/00207543.2017.1292064" @default.
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