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A self-learning particle swarm optimization algorithm with multi-strategy selection

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Abstract

Particle swarm optimization has been widely utilized to tackle various real optimization problems as an effective and simple optimization approach. However, the phenomenon of premature convergence has always existed. To ameliorate the drawback, a self-learning particle swarm optimization algorithm with multi-strategy selection (MSLSPSO) is proposed in this study. In MSLSPSO, an elite particles guidance strategy is proposed. This strategy makes elite particles participate in the search process to strengthen the guidance of the population. A Lévy Flight perturbation strategy is designed, which utilizes the random walk characteristic of Lévy Flight to perturb the historical and global optimal of particles, increasing the diversity of the population. In addition, a fitness distance correlation self-learning mechanism is proposed, which can self-learning according to the characteristics of the population in the search process. A set of test functions are utilized for experimental analysis and compared with four well-known PSO variants. Experimental results show that the MSLSPSO algorithm has efficient robustness and competitive solutions can be obtained.

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References

  1. Robinson J, Rahmat-Samii Y (2004) Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag 52(2):397–407

    Article  MathSciNet  MATH  Google Scholar 

  2. Qin Q, Cheng S, Chu X et al (2017) Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization. Appl Soft Comput 59:229–242

    Article  Google Scholar 

  3. Das P, Behera HS, Panigrahi BK (2016) A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol Comput 28:14–28

    Article  Google Scholar 

  4. Chen K, Zhou F, Yin L et al (2018) A hybrid particle swarm optimizer with sine cosine acceleration coefficients. Inf Sci 422:218–241

    Article  MathSciNet  Google Scholar 

  5. Xu L, Song B, Cao M (2021) An improved particle swarm optimization algorithm with adaptive weighted delay velocity. Syst Sci Control Eng 9(1):188–197

    Article  Google Scholar 

  6. Chen Y, Li L, Peng H et al (2018) Dynamic multi-swarm differential learning particle swarm optimizer. Swarm Evol Comput 39:209–221

    Article  Google Scholar 

  7. Mohapatra P, Das KN, Roy S (2017) A modified competitive swarm optimizer for large scale optimization problems. Appl Soft Comput 59:340–362

    Article  Google Scholar 

  8. Esmin AAA, Matwin S (2013) HPSOM: a hybrid particle swarm optimization algorithm with genetic mutation. Int J Innov Comput Inf Control 9(5):1919–1934

    Google Scholar 

  9. Epitropakis MG, Plagianakos VP, Vrahatis MN (2012) Evolving cognitive and social experience in particle swarm optimization through differential evolution: a hybrid approach. Inf Sci 216:50–92

    Article  Google Scholar 

  10. Zhou Z, Shi YH (2011) Inertia weight adaption in particle swarm optimization algorithm. Lect Notes Comput Sci 6728(1):71–79

    Article  Google Scholar 

  11. Zhan Z, Zhang J, Li Y et al (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern 39(6):1362–1381

    Article  Google Scholar 

  12. Agrawal A, Tripathi S (2019) Particle swarm optimization with probabilistic inertia weight. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms. Springer, Singapore, pp 239–248

    Chapter  Google Scholar 

  13. Liu H, Zhang XW, Tu LP (2020) A modified particle swarm optimization using adaptive strategy. Expert Syst Appl 152:113353

    Article  Google Scholar 

  14. Li W, Meng X, Huang Y et al (2020) Multipopulation cooperative particle swarm optimization with a mixed mutation strategy. Inf Sci 529:179–196

    Article  MathSciNet  MATH  Google Scholar 

  15. Li W, Meng X, Huang Y et al (2021) Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies. Complex Intell Syst 7(3):1223–1239

    Article  Google Scholar 

  16. Zeng N, Wang Z, Liu W (2020) A dynamic neighborhood-based switching particle swarm optimization algorithm. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3029748

    Article  Google Scholar 

  17. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufman Academic Press, San Matteo

    Google Scholar 

  18. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948.

  19. Wright S (1932) The roles of mutation, inbreeding, crossbreeding, and selection in evolution. 355–366

  20. Li W, Meng X, Huang Y (2021) Fitness distance correlation and mixed search strategy for differential evolution. Neurocomputing 458:514–525

    Article  Google Scholar 

  21. Jones T, Forrest S (1995) Genetic algorithms and heuristic search. Santa Fe Inst Techn Rep 95–02:21

    Google Scholar 

  22. Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the sixth international conference on genetic algorithms, pp 184–192

  23. Hakl H, Uguz H (2014) A novel particle swarm optimization algorithm with Lévy Flight. Appl Soft Comput 23:333–345

    Article  Google Scholar 

  24. Jensi R, Jiji W (2016) An enhanced particle swarm optimization with Lévy Flight for global optimization. Appl Soft Comput 43:248–261

    Article  Google Scholar 

  25. Jamil Yang MXS (2013) A literature survey of benchmark functions for global optimization problems. Mathematics 4(2):150–194

    MATH  Google Scholar 

  26. Mohammad H, Mohammad RM, Mohammad ME (2013) Adaptive cooperative particle swarm optimizer. Appl Intell 39:397–420

    Article  Google Scholar 

  27. Chen X, Tianfield H, Mei C et al (2017) Biogeography based learning particle swarm optimization. Soft Comput 21(24):7519–7541

    Article  Google Scholar 

  28. Liang JJ, Qin A, Suganthan PN et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  29. Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62066019, 61903089), the Natural Science Foundation of Jiangxi Province (Grant Nos. 20202BABL202020, 20202BAB202014), the National Key Research and Development Program of China (Grant No. 2020YFB1713700) and the Graduate Innovation Foundation of JiangXi University of Science and Technology (Grant No. XY2021-S094).

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Authors and Affiliations

  1. School of Information Engineering, JiangXi University of Science and Technology, Ganzhou, China

    Bo Sun & Wei Li

  2. School of Economic and Management, JiangXi University of Science and Technology, Ganzhou, China

    Yue Zhao

  3. School of Mathematical and Computer Science, Gannan Normal University, Ganzhou, China

    Ying Huang

Authors
  1. Bo Sun
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  2. Wei Li
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  3. Yue Zhao
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  4. Ying Huang
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Contributions

BS: Data curation, writing—original draft preparation. WL: Conceptualization, methodology, and validation. YZ: Supervision, Writing—Editing and Reviewing. YH: Visualization, software, investigation.

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Correspondence to Yue Zhao.

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Sun, B., Li, W., Zhao, Y. et al. A self-learning particle swarm optimization algorithm with multi-strategy selection. Evol. Intel. 16, 1487–1502 (2023). https://doi.org/10.1007/s12065-022-00755-6

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  • DOI: https://doi.org/10.1007/s12065-022-00755-6

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