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. 2022 Aug 25;17(8):e0273155.
doi: 10.1371/journal.pone.0273155. eCollection 2022.

Dynamic elite strategy mayfly algorithm

Qianhang Du  1 , Honghao Zhu  1
Affiliations

Dynamic elite strategy mayfly algorithm

Qianhang Du et al. PLoS One. .

Abstract

The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability.

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Conflict of interest statement

The authors state that no competing interests exist.

Figures

Fig 1
Fig 1. Flow chart of the DESMA.
Fig 2
Fig 2. The average convergence curve of the algorithm under different calculation examples ((1)-(28) correspond to functions f1-f28 respectively).
Fig 3
Fig 3. Comparison statistics of algorithm running time under different algorithm.
See this image and copyright information in PMC

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