Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators
| Autor: | Sha-Sha Guo, L. F. Zhu, H. Y. Wang, Jie-Sheng Wang, Meng-wei Guo, Wei Xie |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science 020209 energy Foraging convergence factor 02 engineering and technology Swarm intelligence bat algorithm Clustering hunting mechanism 0202 electrical engineering electronic engineering information engineering General Materials Science Local search (optimization) Cluster analysis Global optimization Bat algorithm business.industry General Engineering Particle swarm optimization Pattern recognition sinusoidal position update strategy Maxima and minima Unsupervised learning Harmony search 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 80536-80560 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms. |
| Databáze: | OpenAIRE |
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