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Set-based discrete particle swarm optimization and its applications: a survey

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Abstract

Particle swarm optimization (PSO) is one of the most popular population-based stochastic algorithms for solving complex optimization problems. While PSO is simple and effective, it is originally defined in continuous space. In order to take advantage of PSO to solve combinatorial optimization problems in discrete space, the set-based PSO (S-PSO) framework extends PSO for discrete optimization by redefining the operations in PSO utilizing the set operations. Since its proposal, S-PSO has attracted increasing research attention and has become a promising approach for discrete optimization problems. In this paper, we intend to provide a comprehensive survey on the concepts, development and applications of S-PSO. First, the classification of discrete PSO algorithms is presented. Then the S-PSO framework is given. In particular, we will give an insight into the solution construction strategies, constraint handling strategies, and alternative reinforcement strategies in S-PSO together with its different variants. Furthermore, the extensions and applications of S-PSO are also discussed systemically. Some potential directions for the research of S-PSO are also discussed in this paper.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61622206 and 61379061), in part by the Natural Science Foundation of Guangdong (2015A030306024), in part by the Guangdong Special Support Program (2014TQ01X550), and in part by the Guangzhou Pearl River New Star of Science and Technology (201506010002).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Wei-Neng Chen & Da-Zhao Tan

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  1. Wei-Neng Chen
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Correspondence to Wei-Neng Chen.

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Wei-Neng Chen received the bachelor’s and PhD degrees from Sun Yat-sen University, China in 2006 and 2012, respectively. He is currently a professor with the School of Computer Science and Engineering, South China University of Technology, China. His current research interests include swarm intelligence algorithms and their applications on cloud computing, operations research, and software engineering. He has published over 70 papers in international journals and conferences. Dr. Chen was an awardee of the NSFC Excellent Young Scholars Program in 2016. He also received the IEEE Computational Intelligence Society Outstanding Dissertation Award for his doctoral thesis in 2016.

Da-Zhao Tan received the bachelor’s degree from Shenzhen University, China in 2017. He is currently working towards the Master’s degree in School of Computer Science and Engineering, South China University of Technology, China.

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Chen, WN., Tan, DZ. Set-based discrete particle swarm optimization and its applications: a survey. Front. Comput. Sci. 12, 203–216 (2018). https://doi.org/10.1007/s11704-018-7155-4

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