Firefly algorithm (FA) is a novel population-based stochastic optimization algorithm and has been shown to yield good performance for solving varieties of optimization problems. Meanwhile, it sustains premature convergence because it is easily to fall into the local optima which may generate a low accuracy of solution or even fail. To overcome this defect, a nonlinear time-varying step strategy for firefly algorithm (NTSFA) is presented. It uses a nonlinear decreasing and time-varying step-size for fireflies to better balance the algorithm's ability of exploration and exploitation. Numerical simulation on 20 test benchmark functions display that the proposed algorithm can increase the accuracy of the original FA. Finally, we apply NTSFA to integrate into k-means clustering for mouse dataset. The results show that NTSFA is an effective optimization algorithm.
Firefly Algorithm is a nature-inspired optimization method, which has been shown to implement well on numerous optimization problems. But it can easily fall into the local optima and low precision. Therefore, it is very important to overcome these defects. In this paper, we use a dynamic strategy for step setting, which takes into account the population diversity of fireflies. The experiments show that the proposed algorithm improves the performance of original firefly algorithm.
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