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Spark-based parallel many-objective service process optimization for correlation-aware cloud manufacturing
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DOI：10.1080/0951192X.2025.2497476.png)
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Service process optimization is a key area of research in cloud manufacturing (CMfg), with the aim of integrating manufacturing services for manufacturing tasks efficiently. Although there is a large body of research devoted to this field, significant challenges remain. These include the complexities of accounting for inter-service correlation and sustaining performance amid the increasing number of services and diversification of Quality of Service (QoS) criteria. To effectively cope with these difficulties, this paper presents a novel many-objective optimization method. It develops improved meta-heuristic strategies to get a good balance between convergence and diversity and leverages the Spark parallel computing environment to enhance search efficiency. The experimental results show that the proposed approach improves convergence and diversity in complex correlation-aware service process scenarios compared to the selected representative methods. Specifically, IGD, Spread and GD metrics increase by 25.13%, 21.70%, and 80.60%, respectively, while the computational time is reduced by 39.78%.
Keyword:
Many-objective
service process optimization
parallel
inter-service correlation
cloud manufacturing
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2.3K
被引数:
3.4K
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