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Using an Improved dynamic learning evolutionary algorithm to Solve Many-objective Service Resource Combination Optimization Problem in Crowdsourcing Design 36

Using an Improved Dynamic Learning Evolutionary Algorithm to...

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36th Chinese Control and Decision Conference (CCDC)

作者： Li, Gui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Hubei Peoples R China

ISBN: (纸本)9798350387780;9798350387797

With the development of social productive forces, a new service-oriented crowdsourcing design model is emerging. The quality of task implementation and consumer satisfaction greatly depend on the optimal selection of service resources in crowdsourcing design processes. The current research primarily focuses on optimizing quality of service (QoS) such as time and cost, either through single-objective or multi-objective optimization. As individual needs increase, the requirements for different QoS also increase gradually. This leads to a many-objective combination optimization problem. Thus, this paper models and solves the problem from the perspective of many-objective optimization. Firstly, a many-objective optimization model is constructed according to the characteristics of service resources in crowdsourcing design, and an evaluation system of crowdsourcing design service resources is established. Secondly, a dynamic learning evolutionary algorithm based on adaptive resource allocation (DLEA-ARA) is proposed to adjust the computing resources according to the convergence difficulty of the problem. At the same time, niche technology is introduced to enhance the diversity maintenance ability. Finally, the proposed DLEA-ARA is compared with other state-of-the-art many-objective evolutionary algorithms (MaOEAs) under simulation data sets, and the results show the superiority of the proposed DLEA-ARA.

关键词： many-objective optimization dynamic learning evolutionary algorithm crowdsourcing design service resource combination optimization

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Correlation-aware constrained many-objective service composition in crowdsourcing design

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Li, Gui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

With the development of social productivity, crowdsourcing design, a new service-oriented innovative design model emerged. The optimal selection of service resources in crowdsourcing design has a great impact on the quality of task implementation and consumer satisfaction. Unlike the previous research, this paper models and solves the problem from the perspective of constrained many-objective optimization. Firstly, a constrained 6objective combination optimization model is constructed according to the characteristics of service resources in crowdsourcing design. Secondly, a variety of correlation categories and correlation forms of services are considered. It is not limited to the correlation between two services but rather correlations between any number of services. In order to improve search efficiency, a multi-objective service pruning (MOSP) method based on efficient non-dominated sorting (ENS) is used to reduce the decision space. The new constraint strategy handles complex constraints, and a correlation-aware local search strategy is proposed to deal with correlations between services. The results of comparison experiments prove the superiority of the proposed method in solving the constrained correlation-aware many-objective service composition problem.

关键词： Many-objective optimization dynamic learning evolutionary algorithm Crowdsourcing design Service composition Constraint optimization

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