A preference-based multi-objective algorithm for optimal service composition selection in cloud manufacturing

ABSTRACT

Aiming at addressing several conflicting criteria of quality of service (QoS) that should be trade-off optimized during service composition and optimal selection (SCOS) in cloud manufacturing (CMfg), the improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed and employed to address the SCOS issue. This is the first time that a preference-based multi-objective algorithm has been used to address the SCOS problem. In this paper, a novel K-layer preference reference point set approach is proposed for generating a reference point set with this algorithm to guide the search towards the interesting parts of the Pareto optimal region based on customer preferences, which improves the efficiency of the algorithm and the convergence of the obtained solution. A new fitness assignment strategy and environment selection scheme is developed accordingly to balance the relationship of diversity and convergence of preserved individuals in each generation. Additionally, the memetic algorithm is integrated into the evolutionary mechanism of the algorithm to address the insufficiency of local search. To validate the performance of the proposed algorithm, several test cases are conducted. The results demonstrate that the proposed algorithm is more competitive than other considered algorithms.

KEYWORDS:

• Cloud manufacturing (CMfg)
• service composition and optimal selection (SCOS)
• quality of service (QoS)
• preference-based multi-objective algorithm
• non-dominated sorting genetic algorithm-III (NSGA-III)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science and Technology Major Project of China under Grant [number 2018ZX04002001-009].