A hybrid modified step Whale Optimization Algorithm with Tabu Search for data clustering
| Autor: | Amr Mohamed AbdelAziz, Kareem Kamal A. Ghany, Adel Abu El-Magd Sewisy, Taysir Hassan A. Soliman |
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| Rok vydání: | 2022 |
| Předmět: |
Optimization problem
General Computer Science Computer science Swarm behaviour Particle swarm optimization 020206 networking & telecommunications Dunn index 02 engineering and technology computer.software_genre Swarm intelligence Tabu search Local optimum 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Cluster analysis computer |
| Zdroj: | Journal of King Saud University - Computer and Information Sciences. 34:832-839 |
| ISSN: | 1319-1578 |
| Popis: | Clustering is a popular data analysis tool that groups similar data objects into disjoint clusters. Clustering algorithms have major drawbacks, such as: sensitivity to initialization and getting trapped in local optima. Swarm Intelligence (SI) have been combined with clustering techniques to enhance their performance. Despite their improvements, some SI methods, such as Particle Swarm Optimization (PSO) and its rivals, had their drawbacks, such as low convergence rates and generating low quality solutions. This is due to storing a single best solution about solution space, which can lead swarm members to local optima. Whale Optimization Algorithm (WOA) is a recent SI method, which has been proved as a global optimizer over multiple optimization problems. Unlikely, WOA uses the same concept as PSO, it uses the best solution to reposition swarm members. To overcome this problem, WOA needs to store multiple best solutions about solution space. Tabu Search (TS) is a meta-heuristic method, which uses memory components to explore and exploit search space. In this paper, we propose combining WOA with TS (WOATS) for data clustering. To assess the efficiency of WOATS, it has been applied in data clustering. WOATS uses an objective function inspired by partitional clustering to maintain the quality of clustering solutions. WOATS was tested over multiple real life datasets, which their sizes vary from small to large-sized datasets. WOATS was able to find centers with high quality in a small number of iterations regardless of the size of the dataset, which clarifies its ability to cover the solution space efficiently. The generated clusters were evaluated according to different outlier criteria: Davies-Bouldin Index and Dunn Index. According to these criteria, WOATS presented its superiority over multiple recent original and hybrid SI methods for different datasets. Results obtained by WOATS were better than the results of the other methods with big differences regarding to the different outlier criteria, which ensured the efficiency of WOATS. |
| Databáze: | OpenAIRE |
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