Binary hybrid particle swarm optimization withwavelet mutation
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conference contribution
posted on 2024-06-04, 14:47 authored by QA Tran, QD Dinh, Frank JiangFrank Jiang© Springer International Publishing Switzerland 2015. Particle swarm optimization (PSO) is an evolutionary algorithmin 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 withWavelet 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.
History
Volume
326Pagination
261-272Location
Hanoi, VietnamPublisher DOI
Start date
2014-10-09End date
2014-10-11ISSN
2194-5357ISBN-13
9783319116792Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
Nguyen VH, Le AC, Huynh VNTitle of proceedings
KSE 2014 : Knowledge and Systems Engineering : Proceedings of the Sixth International ConferenceEvent
Knowledge and Systems Engineering. Conference (2014 : 6th : Hanoi, Vietnam)Publisher
SpringerPlace of publication
Berlin, GermanySeries
Advances in intelligent systems and computingUsage metrics
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