| Autor: |
Ibrahim Al-Shourbaji, Pramod Kachare, Sajid Fadlelseed, Abdoh Jabbari, Abdelazim G. Hussien, Faisal Al-Saqqar, Laith Abualigah, Abdalla Alameen |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
International Journal of Computational Intelligence Systems, Vol 16, Iss 1, Pp 1-24 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1875-6883 |
| DOI: |
10.1007/s44196-023-00279-6 |
| Popis: |
Abstract Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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