首页|Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition
Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition
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NSTL
Elsevier
Cloud manufacturing (CMfg) has received increasingly attention from both academia and industry. Cloud service composition is a critical technique in CMfg that connects different available manufacturing cloud services (MCSs) to generate a composite manufacturing cloud service (CMCS) to satisfy users' requirements. Many available MCSs with the same or similar functionality but different QoS attributes are deployed in the CMfg platform. So it is challenging to obtain an optimal CMCS to satisfy the users' complex requirements. Considerable numbers of approaches have been proposed to solve this problem. However, most of them often fall in a local optimum instead of the global one. In this paper, a novel eagle strategy using uniform Mutation and modified Whale Optimization Algorithm (MWOA) is proposed to maintain a balance between the global and local search abilities. In this approach, the uniform mutation is applied to perform the global search to preserve the diversification of the population, and a modified whale optimization algorithm is designed to perform the local search. The performance of the new approach is verified on various benchmark functions and different scales of QoS-aware cloud service composition problems. The experimental results demonstrate that the proposed MWOA has superior performance over the other methods. (C) 2021 Published by Elsevier B.V.
Cloud manufacturingQuality of service (QoS)Service compositionEagle strategyModified whale optimization algorithmLEARNING-BASED OPTIMIZATIONARTIFICIAL BEE COLONYSELECTIONDESIGNONTOLOGY
NSTL
Elsevier
