School of Computer and Information Engineering, Bengbu University, Bengbu, Anhui 233030, China.
PLoS One. 2022 Aug 25;17(8):e0273155. doi: 10.1371/journal.pone.0273155. eCollection 2022.
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.
蜉蝣算法（MA）作为一种新提出的智能优化算法，容易陷入局部最优和收敛速度慢等问题。针对这些问题，本文提出了一种基于动态精英策略的改进蜉蝣算法（DESMA）。具体来说，它首先确定当前种群中最佳蜉蝣的具体空间，并动态设置搜索半径。然后在这个范围内生成一定数量的精英蜉蝣。最后，当精英蜉蝣的适应度值优于最佳蜉蝣时，从新生成的精英蜉蝣中选择最好的一个来替代当前种群中的最佳蜉蝣。在 CEC2013 的 28 个标准基准测试函数上的实验结果表明，我们提出的算法在准确性、速度和稳定性方面都优于其他算法。
