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首页> 外文期刊>International Journal of Production Research >A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition
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A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

机译：结合人工蜂群和布谷鸟搜索算法的混合方法实现多目标云制造服务组合

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摘要

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.
机译：本文提出了一种用于云制造中服务组合和最优选择（SCOS）的多目标混合人工蜂群（MOHABC）算法，其中从经济和环境的角度来考虑服务质量和能耗可持续制造的两个支柱。 MOHABC使用帕累托优势的概念来指导蜂群的搜索，并维护在外部档案中找到的非主导解决方案。为了在Pareto前沿实现解决方案的良好分布，在采用的蜜蜂搜索中引入了使用Levy飞行的杜鹃搜索，以保持种群的多样性。此外，为了确保MOHABC的开发和勘探能力之间的平衡，在旁观者搜索中设计了全面的学习策略，以便每只蜜蜂从外部档案馆精英，其本身和其他旁观者那里学习。进行实验以验证改进策略和参数影响对所提出算法的影响，并针对MOCOS与典型的多目标算法对SCOS问题进行了比较研究。结果表明，所提出的方法获得了非常有前途的解决方案，大大超过了其他已考虑的算法。

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