Adaptive Mutation Dynamic Search Fireworks Algorithm
AbstractThe 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Li, X.-G.; Han, S.-F.; Zhao, L.; Gong, C.-Q.; Liu, X.-J. Adaptive Mutation Dynamic Search Fireworks Algorithm. Algorithms 2017, 10, 48.
Li X-G, Han S-F, Zhao L, Gong C-Q, Liu X-J. Adaptive Mutation Dynamic Search Fireworks Algorithm. Algorithms. 2017; 10(2):48.Chicago/Turabian Style
Li, Xi-Guang; Han, Shou-Fei; Zhao, Liang; Gong, Chang-Qing; Liu, Xiao-Jing. 2017. "Adaptive Mutation Dynamic Search Fireworks Algorithm." Algorithms 10, no. 2: 48.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.