A novel hybrid algorithm for large-scale composition optimization problems in cloud manufacturing. Issue 9 (2nd September 2021)

• Record Type: Journal Article
• Authors: Wang, Zhongning; Wang, Shilong; Yang, Bo; Wang, Yankai; Chen, Ronghua
• Is Part Of: International journal of computer integrated manufacturing. Volume 34:Issue 9(2021)
• Page Start: 898
• Page End: 919
• Publication Date: 2021-09-02
• Subjects: Cloud manufacturing -- cloud service composition and optimization -- quality of service -- ABCSA -- random-evolve simplex method -- global best guided and chaos searching strategy; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2021.1946852
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: At present, with the emergence and development of cloud manufacturing (CMfg), the scale of services in CMfg platforms increases rapidly which provide the same or familiar functionality but different performance. Large-scale cloud service composition and optimization (CSCO) problems is one of the key issues for the implementation of CMfg. To deal with this NP-hard problem, a novel hybrid algorithm called Bee-Colony Simplex method hybrid Algorithm (ABCSA) for CSCO problems is proposed in this paper, which employs both the Simplex method and chaotic and global best guided strategy. The random-evolve Simplex method is proposed to maintain the algorithm work efficiently to keep the population diversity and avoid premature convergence. The global best guided and chaos searching strategy is proposed to avoid local optimization. To evaluate the effectiveness and efficiency, simulation and analysis of the experiments are carried out, and the results clearly prove the superior performance of ABCSA over existing intelligent optimization algorithms in the CSCO problems.