Adaptive Step Firefly Algorithm Based on Population Diversity

As a new swarm intelligence optimization method, firefly algorithm shows good performance on many complex optimization problems. However, due to the fixed parameters of FA, it is difficult to adapt to environmental changing during the iteration process, and FA easily lose its diversity and lead to premature convergence. In this paper, an adaptive step firefly algorithm based on population diversity called DASFA is proposed to improve the performance of FA. The DASFA designed an adaptive step which is decreasing as the search process and regulated by population diversity, it could help the algorithm maintains high diversity to getting out of the local optimal and finding the optimal value eventually. Experiments are conducted on ten classic benchmark functions, the results show that DASFA achieves better performance than FA and some its variants.