A New Firefly Algorithm with Enhanced Attractiveness
Jianxun Liu, Jinfei Shi, Fei Hao, Min Dai, Zhisheng Zhang · 2021
In the process of continuous optimization of complex problems, the firefly algorithm (FA) has the disadvantages of poor convergence behavior and easy to fall into local optimality. In order to overcome the shortcomings, we propose a new firefly algorithm with enhanced attractiveness (EA-LFA). It is achieved by reconstructing a new and enhanced attractive item and introducing Lévy flight to FA at the same time. Compared with the FA and its variants, the EA-LFA has best convergence behavior and global optimization efficiency.