An Improved Firefly Algorithm with Adjustable Step and Double-bottom Map
Jiexia Wang, Guangbin Zhang · 2021
Firefly algorithm has been widely used in many optimization problems since it was proposed because of its good searching ability. However, in the searching process, the standard firefly algorithm can easily fall into local optimal because a fixed step value is used. To solve this problem, an improved firefly algorithm is proposed in this paper by introducing an adjustable strategy for step size setting to improve the optimization accuracy and a double-bottom map to accelerate the convergence. In order to test the performance of the algorithm, twelve benchmark functions are used. Compared to other algorithms, the experimental results show that the modified firefly algorithm has better convergence capabilities and higher accuracy.