深度学习萤火虫算法

Firefly Algorithm with Deep Learning

为克服萤火虫算法全局寻优精度不高和过早收敛的缺点,本文提出深度学习萤火虫算法.算法采用随机吸引模型,萤火虫随机选择一个粒子学习,根据历史最优位置构建广义中心粒子,对其进行一定次数的单维深度学习,学习后的粒子引导种群进化.实验发现,深度学习策略及粒子深度学习次数对算法优化性能的改善起着重要作用.12个基准测试函数的实验结果表明,算法的综合寻优性能优于其它8种最近提出的萤火虫算法.

In order to overcome low precision and premature convergence of firefly algorithm,this paper proposes a new method,called firefly algorithm with deep learning.First,firefly algorithm selects a particle to learn according to the random attraction model;second,the method constructs a general center particle based on the best historical position;third,the particle leads the evolution of the population after a certain times of one-dimensional deep learning.Experiments show that the deep learning strategy and the number of deep learning of particles play an important role in optimizing the performance of the algorithm.The experimental results of 12 benchmark functions demonstrate that the comprehensive optimization performance of the proposed algorithm outperforms eight other recently firefly algorithm variants.