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.