The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving\noptimization problems. However, dynFWA easily falls into local optimal solutions prematurely and it\nalso has a slow convergence rate. In order to improve these problems, an adaptive mutation dynamic\nsearch fireworks algorithm (AMdynFWA) is introduced in this paper. The proposed algorithm\napplies the Gaussian mutation or the Levy mutation for the core firework (CF) with mutation\nprobability. Our simulation compares the proposed algorithm with the FWA-Based algorithms and\nother swarm intelligence algorithms. The results show that the proposed algorithm achieves better\noverall performance on the standard test functions.
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