Abstract
Particle swarm optimization (PSO) is an evolutionary algorithm in which individuals, called particles, move around a multi-dimensional problem space at different directions (trajectories) and speeds (velocities) to find the best solution. A particle movement is based on its previous best result and the previous best result of the entire population. In one of PSO variants – the HPSOWM [4], a mutation process based on wavelet theory was added to the original PSO to prevent premature conclusion of the best solution. This hybrid PSO has improved solution stability and quality over the original algorithm as well as many other hybrid PSO algorithms. However, it is limited to work on a continuous problem space. In this paper, we propose Binary Hybrid Particle Swarm Optimization with Wavelet Mutation (BHPSPWM) – a reworked version of such algorithm which operates on binary-based problem space. The movement mechanisms of particles as well as the mutation process have been transformed. The new algorithm was applied in training block-based neural network (BBNN) as well as finding solutions for several mathematical functions. The results showed significant improvement over genetic algorithms.
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Tran, QA., Dinh, Q.D., Jiang, F. (2015). Binary Hybrid Particle Swarm Optimization with Wavelet Mutation. In: Nguyen, VH., Le, AC., Huynh, VN. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-319-11680-8_21
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DOI: https://doi.org/10.1007/978-3-319-11680-8_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11679-2
Online ISBN: 978-3-319-11680-8
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