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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing
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A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing

机译：一种具有进化型运算符的混合灰狼优化算法，可实现最佳QoS感知服务组成和云制造中的最优选择

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摘要

Cloud manufacturing (CMfg), as a new service-oriented technology, is aiming towards delivering on-demand manufacturing services over the internet by facilitating collaboration among different producers with distributed manufacturing resources and capabilities. To this end, addressing service composition and optimal selection (SCOS) problem has been the pivotal challenge. This NP-hard combinatorial problem deals with selecting and combining the available resources into a composite service to meet the user's requirements while keeping up the optimal quality of service. This study proposes a new hybrid approach based on the recently developed grey wolf optimizer (GWO) algorithm and evolutionary operators of the genetic algorithm. The embedded crossover and mutation operators carry out a twofold functionality: (1) they make it possible to adapt the continuous structure of GWO to a combinatorial problem such as SCOS, and (2) they help to avoid the local optimal stagnation at the hunting process by providing more exploration strength. A series of experiments were designed and conducted to prove the effectiveness of the proposed algorithm, and the experimental results demonstrated that the proposed algorithm delivers superior performance compared with that of both existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.
机译：作为一种新的面向服务技术的云制造（CMFG）旨在通过促进不同生产者的合作，通过具有分布式制造资源和能力的不同生产者的合作来提供按需制造服务。为此，解决服务成分和最佳选择（SCOS）问题是关键挑战。此NP-COMPININALATIAL问题处理和将可用资源的选择和组合成复合服务，以满足用户的要求，同时保持最佳服务质量。本研究提出了一种基于最近开发的灰狼优化器（GWO）算法和遗传算法的进化运营商的新的混合方法。嵌入式交叉和突变操作员进行双重功能：（1），它们使得可以使GWO的连续结构适应组合问题，例如SCOS，（2）他们有助于避免在狩猎过程中避免局部最佳停滞物通过提供更多的探索力量。设计并进行了一系列实验，以证明该算法的有效性，实验结果表明，该算法与GWO和遗传算法的现有离散变化相比，该算法提供了卓越的性能，特别是在大规模的SCOS问题中。

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