One-dimensional deep learning firefly algorithm guided by the best particle

摘要

We propose the one-dimensional deep learning firefly algorithm guided by the best particle in order to increase the convergence speed and optimisation precision of the firefly algorithm. In each generation of optimisation process, the optimal particle is first updated in a fixed number of times according to the newly designed update formula. The update mode is defined as single-dimensional deep learning. After the optimal particle completes single-dimensional deep learning, other fireflies in the population keep the original evolutionary way to update the location and iteratively complete the optimisation task. Experiments with 12 benchmark functions show that the proposed algorithm has a higher optimisation capacity than the other six modified firefly algorithms.