一种精英反向学习的萤火虫优化算法

为了提高传统萤火虫算法的收敛速度和求解精度，提出了一种精英反向学习的萤火虫优化算法。通过反向学习策略构造精英群体，在精英群体构成的区间上求普通群体的反向解，增加了群体的多样性，提高了算法的收敛速度；同时，为了避免最优个体陷入局部最优，使整个群体在搜索过程中出现停滞，提出了差分演化变异策略；最后，提出了一种线性递减的自适应步长来平衡算法的开发能力。实验结果表明，算法在收敛速度和收敛精度上有更好的效果。

Firefly optimization algorithm utilizing elite opposition-based learning

To increase the convergence speed and solution accuracy of the traditional firefly algorithm, in this paper, we propose a firefly optimization algorithm that utilizes elite opposition-based learning. Using an opposition-based learning strategy, we constructed an elite group and, in the interval of the elite group, we solved the opposite solutions of the ordinary groups. This strategy could increase group diversity and improve the convergence speed of the algorithm. To prevent the optimal individual from falling into the local optimum, which could cause stagnation of the whole group during the search process, we introduce a differential evolutionary mutation strategy. Finally, we propose an adaptive step size with a linear decrease to balance the development ability of the algorithm. Experimental results show that the proposed algorithm can increase convergence speed and accuracy.