Diversity collaboratively guided random drift particle swarm optimization

Chao Li, Jun Sun, Vasile Palade, Li-Wei Li

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1 Citation (Scopus)
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The random drift particle swarm optimization (RDPSO) algorithm is an effective random search technique inspired by the trajectory analysis of the canonical PSO and the free electron model in metal conductors placed in an external electric field. However, like other PSO variants, the RDPSO algorithm also inevitably encounters premature convergence when solving multimodal problems. To address this issue, this paper proposes a novel diversity collaboratively guided (DCG) strategy for the RDPSO algorithm that enhances the search ability of the algorithm. In this strategy, two kinds of diversity measures are defined and modified in a collaborative manner. Specifically, the whole search process of the RDPSO is divided into three phases based on the changes in the two diversity measures. In each phase, different values are selected for the key parameters of the update equation in the RDPSO to make the particle swarm perform different search modes. Consequently, the improved RDPSO algorithm with the DCG strategy (DCG-RDPSO) can maintain its diversity dynamically at a certain level, and thus can search constantly without stagnation until the search process terminates. The performance evaluation of the proposed algorithm is done on the CEC-2013 benchmark suite, in comparison with several versions of RDPSO, different variants of PSO and several non-PSO evolutionary algorithms. Experimental results show that the proposed DCG strategy can significantly improve the performance and robustness of the RDPSO algorithm for most of the multimodal problems. Further experiments on economic dispatch problems also verify the effectiveness of the DCG strategy.
Original languageEnglish
Pages (from-to)2617–2638
Number of pages22
JournalInternational Journal of Machine Learning and Cybernetics
Issue number9
Publication statusPublished - 13 Jul 2021

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/s13042-021-01345-1

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.


National Natural Science Foundation of China (Projects Numbers: 61673194, 61672263, 61672265), and in part by the national first-class discipline program of Light Industry Technology and Engineering (Project Number: LITE2018-25).


  • Diversity guided strategy
  • Random drift particle swarm optimization algorithm
  • Multimodal optimization problems
  • Economic dispatch problems


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