A Novel Service Composition Algorithm for Cloud-Based Manufacturing Environment

作     者：Zhu, Linan Li, Penghang Shen, Guojiang Liu, Zhi 

作者机构：Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：39148-39164页

核心收录：

基　　金：National Natural Science Foundation of China Zhejiang Provincial Natural Science Foundation of China [LY16F020033] Zhejiang Province Basic Public Welfare Research Project [LGG20F030008] 

主　　题：Manufacturing Cloud computing Task analysis Optimization Dynamic scheduling Quality of service Cloud manufacturing cloud manufacturing service composition multi-module subtasks artificial bee colony algorithm 

摘      要：Cloud Manufacturing Service Composition (CMSC) is the key issue and taking an important role in solving the interconnection and interoperability of resources and services for Cloud Manufacturing (CMfg). CMSC is a typical kind of NP-hard problems with the characteristics of dynamic and uncertainty. Solving large scale CMSC problem by using the traditional methods might be not efficient because of the massive complex resources and large-scale searching space. To overcome this shortcoming, a novel artificial bee colony algorithm named Multiple Improvement Strategies based Artificial Bee Colony algorithm (MISABC) is proposed. MISABC improves the performance of classical ABC algorithm through several strategies such as (a) differential evolution strategy (DES), (b) oscillation strategy with classical trigonometric factor (TFOS), (c) different dimensional variation learning strategy (DDVLS), (d) Gaussian distribution strategy (GDS). Meanwhile, to address the CMfg scenario, we also propose a manufacturing service composition scheme named as Multi-Module Subtasks Collaborative Execution for Cloud Manufacturing Service Composition (MMSCE-CMSC). Eight benchmark functions with different characteristics, a comparison study with existed improved ABC algorithms and a case study are used to validate the performance of the algorithm. The results demonstrate the effectiveness of the proposed method for addressing complex CMSC problem in CMfg.