A Switch-Mode Firefly Algorithm for Global Optimization

The firefly algorithm has been successfully used in many optimization problems. However, the standard firefly algorithm uses a fixed randomization parameter in the optimization, which emphasizes more on exploration than exploitation, and hence impacts its convergence. This paper proposes a switchmode firefly algorithm, which first focuses on exploration and then switches to exploitation. A fixed randomization parameter is used in exploration, and a gradually decreasing random randomization parameter is used in exploitation. The condition for the switching from exploration to exploitation is identified automatically. Extensive experiments on 15 benchmark functions were performed to verify the effectiveness of the proposed approach.