Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. Experimental results verify the effectiveness of the proposed algorithm.įirefly Algorithm, Lévy Flights and Global Optimization At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. And then, each firefly achieves local search by following the brighter firefly in its neighbors.
Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. A Firefly colony is divided into several subgroups with different model parameters. To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). Tong, Nan Fu, Qiang Zhong, Caiming Wang, Pengjun
A multi-group firefly algorithm for numerical optimization