Mutated firefly algorithm

In the standard firefly algorithm, every firefly has same parameter settings and its value changes from iteration to iteration. The solutions keeps on changing as the optima are approaching which results that it may fall into local optimum. Furthermore, the underlying strength of the algorithm lies in the attractiveness of less brighter firefly towards the brighter firefly which has an impact on the convergence speed and precision. So to avoid the algorithm to fall into local optimum and reduce the impact of maximum of iteration, a mutated firefly algorithm is proposed in this paper. The proposed algorithm is based on monitoring the movement of fireflies by using different probability for each firefly and then perform mutation on each firefly according to its probability. Simulations are performed to show the performance of proposed algorithm with standard firefly algorithm, based on ten standard benchmark functions. The results reveals that proposed algorithm improves the convergence speed, accurateness and prevent the premature convergence.