Firefly algorithm with dynamically changing connections

Abstract

This study proposes a firefly algorithm with dynamically changing connections (FA-DC). In a standard firefly algorithm (FA), a brightness of each firefly is determined by the objective function, and for any two fireflies, the less brighter one will be always attracted by the brighter one. On the other hand, the fireflies of FA-DC move depending on the connections between fireflies. Even if the brighter firefly exists, the less brighter firefly does not move toward the brighter one when there is no connection between the two fireflies. Furthermore, the connections of FA-DC changes dynamically for every iteration. This effect promotes a diversification of the solutions and avoids the solutions being trapped at local optima. We apply FA-DC to 28 optimization benchmarks from the 2013 Congress on Evolutionary computation (CEC), and we compare it with the conventional FA and the particle swarm optimization (PSO). Simulation results show that FA-DC significantly improves the optimization performance from the conventional FA although FA-DC is a simple algorithm that needs no carefully parameter tuning.