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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(10): 1627-1643
J. Tang, G. Liu, and Q. T. Pan, "A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1627-1643, Oct. 2021. doi: 10.1109/JAS.2021.1004129
Citation: J. Tang, G. Liu, and Q. T. Pan, "A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1627-1643, Oct. 2021. doi: 10.1109/JAS.2021.1004129
shu

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

doi: 10.1109/JAS.2021.1004129
  • 1.

    College of Systems Engineering, National University of Defense Technology, Changsha 410000, China

  • 2.

    School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China

Funds:  This work was supported in part by the National Natural Science Foundation of China (62073330), in part by the Natural Science Foundation of Hunan Province (2019JJ20021, 2020JJ4339) and in part by the Scientific Research Fund of Hunan Province Education Department (20B272)
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    Abstract
  • Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

     

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    • Provide a comprehensive survey of swarm intelligence and represent a categorization scheme
    • Describe the achievements of the swarm intelligence in various applications of related fields
    • Summarize the major strengths and limitations, along with main future research trends

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