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A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

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Abstract

The rapid growing number of web services has posed new challenges for service composition computing. How to combine services that meet the needs of users in the least amount of time from a huge number of candidate services is a hot topic of research today. As a meta-heuristic algorithm for solving optimization problems, salp swarm algorithm (SSA) has been widely applied to case scenarios in different fields due to its simple structure and high performance. However, QoS-aware service composition is a discrete problem and existing methods are not suitable for it. Therefore, in this paper, we propose an improved SSA integrating chaotic mapping method for QoS service composition selection, named CSSA. Through the randomness and ergodicity of chaos, reducing the possibility of falling into local optimum and strengthening the exploitation capability of the algorithm. In addition, a fuzzy continuous neighborhood search method is used to enhance the local search capability of the algorithm which makes the discrete space of service composition in a way similar to continuous space. Finally, two well-known datasets are used to verify the effectiveness of CSSA compared to three advanced algorithms and original SSA. The test results demonstrate that CSSA has significant advantages and it also has satisfactory performance in large scale scenarios.

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Acknowledgements

This research is supported by the Science and Technology Plan Project of Wenzhou, China (No.2020G0055).

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Authors and Affiliations

  1. Department of Computer Science, Wenzhou University, Wenzhou, China

    Jun Li, Hao Ren & Huiling Chen

  2. Wenzhou University of Technology, Wenzhou, China

    ChenYang Li

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  1. Jun Li
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  2. Hao Ren
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  3. ChenYang Li
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  4. Huiling Chen
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Correspondence to Jun Li.

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Li, J., Ren, H., Li, C. et al. A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection. Computing 104, 2031–2051 (2022). https://doi.org/10.1007/s00607-022-01080-7

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