A novel enhanced exploration firefly algorithm for global continuous optimization problems

In the global optimization process of the firefly algorithm (FA), there is a need to provide a fast convergence rate and to explore the search space more effectively. Therefore, we conduct modular analysis of the FA and propose a novel enhanced exploration firefly algorithm (EE-FA), which includes an enhanced attractiveness term module and an enhanced random term module. The attractiveness term module can improve the exploration efficiency and accelerate the convergence rate by enhancing the attraction between fireflies. The random term module improves the exploration efficiency by introducing a damped vibration distribution factor. The EE-FA uses multiple parameters to balance its exploration efficiency and convergence rate. The parameters have a great influence on the performance of the EE-FA. In order to achieve the best performance of the EE-FA, each parameter of the EE-FA needs to be simulated to determine its optimal value. Compared to multiple variants of the FA, the EE-FA has better exploration efficiency and a faster convergence speed. Experimental results reveal that the EE-FA recreated consistently vanquishes the front for 24 benchmark functions and 4 real design case studies in terms of both convergence rate and exploration efficiency.