Frontiers of Computer Science

, Volume 12, Issue 2, pp 203–216 | Cite as

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

Review Article
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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.

Keywords

particle swarm optimization combinatorial optimization discrete optimization swarm intelligence set-based 

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Notes

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).

Supplementary material

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

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina

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