A New Improved Firefly Algorithm for Global Numerical Optimization
Gai‐Ge Wang, Lihong Guo, Hong Duan, Heqi Wang · Journal of Computational and Theoretical Nanoscience · 2013
A novel robust meta-heuristic optimization algorithm, which can be considered as an improvement of the recently developed firefly algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of information exchange between the top fireflies, or the optimal solutions during the process of the light intensity updating. The detailed implementation procedure for this improved meta-heuristic method is also described. Standard benchmarking functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of this improved firefly algorithm (IFA) is superior to or at least highly competitive with the standard firefly algorithm, a differential evolution method, a particle swarm optimizer, and a biogeography-based optimizer. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic FA.