A novel hybrid algorithm for large-scale composition optimization problems in cloud manufacturing

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.

KEYWORDS:

• Cloud manufacturing
• cloud service composition and optimization
• quality of service
• ABCSA
• random-evolve simplex method
• global best guided and chaos searching strategy

Acknowledgments

The presented work was supported by the 2030 Innovation Megaprojects of China (Programme on New Generation Artificial Intelligence) [grant number 2018AAA0101804], Key Project of Technological Innovation and Application Development Plan of Chongqing (Grant No. cstc2019jscx-mbdxX0056).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by  the 2030 Innovation Megaprojects of China (Programme on New Generation Artificial Intelligence) [Grant No. 2018AAA0101804], the Key Project of Technological Innovation and Application Development Plan of Chongqing [Grant No. cstc2019jscx-mbdxX0056] and  the Fundamental Research Funds for the Central Universities (no. 2021CDJKYJH021).