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A novel randomised particle swarm optimizer

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Abstract

The particle swarm optimization (PSO) algorithm is a popular evolutionary computation approach that has received an ever-increasing interest in the past decade owing to its wide application potential. Despite the many variants of the PSO algorithm with improved search ability by means of both the convergence rate and the population diversity, the local optima problem remains a major obstacle that hinders the global optima from being found. In this paper, a novel randomized particle swarm optimizer (RPSO) is proposed where the Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order for the problem space to be explored more thoroughly. With this new strategy, the RPSO algorithm not only maintains the population diversity but also enhances the possibility of escaping the local optima trap. Experimental results demonstrate that the proposed RPSO algorithm outperforms some existing popular variants of PSO algorithms on a series of widely used optimization benchmark functions.

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Acknowledgements

This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the UK-China Industry Academia Partnership Programme under Grant UK-CIAPP-276, the National Natural Science Foundation of China under Grants 61873148 and 61933007, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Zidong Wang

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Weibo Liu, Zidong Wang & Xiaohui Liu

  3. Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, 361005, China

    Nianyin Zeng

  4. School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    Yuan Yuan

  5. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Fuad E. Alsaadi

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  1. Weibo Liu
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  2. Zidong Wang
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  3. Nianyin Zeng
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  4. Yuan Yuan
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  5. Fuad E. Alsaadi
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  6. Xiaohui Liu
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Correspondence to Zidong Wang.

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Liu, W., Wang, Z., Zeng, N. et al. A novel randomised particle swarm optimizer. Int. J. Mach. Learn. & Cyber. 12, 529–540 (2021). https://doi.org/10.1007/s13042-020-01186-4

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