An Ant Colony Approach with Subjective-Objective Weight Optimization for Service Selection

To solve the problem that current service selection algorithms converge slowly and cannot simultaneously consider user requirements as well as service performance, an ant colony approach with subject-objective weight optimization for service selection is proposed. First, we use the subjective and ob...

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Bibliographic Details
Published in2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta) pp. 815 - 822
Main Authors Yan, Xiukun, Li, Jing, Zhu, Ming, Du, Jiayi, Xie, Yong, Zhao, Lianjun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2022
Subjects
ant colony algorithm
Ant colony optimization
Convergence
Design methodology
Heuristic algorithms
objective weight
Optimization
QoS
Quality of service
service selection
subjective weight
Vehicle dynamics
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DOI10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00124

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Summary:To solve the problem that current service selection algorithms converge slowly and cannot simultaneously consider user requirements as well as service performance, an ant colony approach with subject-objective weight optimization for service selection is proposed. First, we use the subjective and objective weight analysis method to preprocess services and convert each service's multiple Quality of Service attribute into a normalized value. Then, three optimizations are proposed for the original ant colony optimization algorithm. The initial pheromone concentration is unevenly distributed according to the integrated value, the influencing factors of the algorithm are changed from fixed values to dynamic adaptive adjustment values, the pheromone update rule is improved. Finally, an enhanced ant colony optimization algorithm is applied to service selection. Experimental results show that the proposed method has a faster convergence rate than other referencing algorithms in dealing with the service selection problem, and the selection result satisfies the users better.
DOI:10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00124