Title: Optimisation of multi-objective cloud manufacturing service selection based on dynamic adaptive bat algorithm

Authors: Jie Dai; Ming Zhu; Jing Li; Lianjun Zhao; Xiubao Zheng

Addresses: College of Computer Science and Technology, Shandong University of Technology, Zibo, China ' College of Computer Science and Technology, Shandong University of Technology, Zibo, China ' College of Computer Science and Technology, Shandong University of Technology, Zibo, China ' College of Computer Science and Technology, Shandong University of Technology, Zibo, China ' College of Computer Science and Technology, Shandong University of Technology, Zibo, China

Abstract: With the new round of changes in global manufacturing, cloud manufacturing, a new intelligent manufacturing model that integrates information manufacturing, cloud computing, and other technologies, has been proposed. It aims to rationalise manufacturing resource utilisation and adapt to complex user requirements. However, current research on service composition in cloud manufacturing mode does not fully consider the evaluation indicators of service quality and users' actual needs and constraints. Therefore, this paper first studies multiple service quality optimisation objectives and constraint problems in practical applications and then proposes a dynamic adaptive bat algorithm based on Gaussian mutation. The algorithm achieves fast convergence and reduces the probability of falling into the local extremum by dynamic random adjustment strategy. The effectiveness of the proposed method is verified by comparative experiments. The experimental results show that the proposed method is superior to other algorithms' solution quality and comprehensive performance.

Keywords: cloud manufacturing; service composition; multi-objective optimisation; inertia weight; adaptive learning; Gaussian mutation; bat algorithm.

DOI: 10.1504/IJWGS.2025.144972

International Journal of Web and Grid Services, 2025 Vol.21 No.1, pp.58 - 87

Received: 27 Jan 2024
Accepted: 21 Nov 2024
Published online: 14 Mar 2025 *

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