Adaptive Mutation Dynamic Search Fireworks Algorithm Article Swipe

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Mutation Computer science Algorithm Mathematical optimization Adaptive mutation Convergence (economics) Fireworks Swarm intelligence Local search (optimization) Local optimum Genetic algorithm Particle swarm optimization Mathematics Biochemistry Economics Economic growth Gene Organic chemistry Chemistry

Xiguang Li , Shoufei Han , Liang Zhao , Changqing Gong , Xiaojing Liu ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.3390/a10020048 · OA: W2611379430

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