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Artificial Ecosyste...
Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems
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- Al-Shourbaji, Ibrahim (författare)
- Jazan Univ, Saudi Arabia; Univ Hertfordshire, England
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- Kachare, Pramod (författare)
- Ramrao Adik Inst Technol, India
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- Fadlelseed, Sajid (författare)
- Univ Hertfordshire, England
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- Jabbari, Abdoh (författare)
- Jazan Univ, Saudi Arabia
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- Hussien, Abdelazim (författare)
- Linköpings universitet,Programvara och system,Tekniska fakulteten,Fayoum Univ, Egypt
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- Al-Saqqar, Faisal (författare)
- Al Al Bayt Univ, Jordan
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- Abualigah, Laith (författare)
- Al Al Bayt Univ, Jordan; Sunway Univ Malaysia, Malaysia; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia
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- Alameen, Abdalla (författare)
- Prince Sattam Bin Abdulaziz Univ, Saudi Arabia
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(creator_code:org_t)
- SPRINGERNATURE, 2023
- 2023
- Engelska.
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Ingår i: International Journal of Computational Intelligence Systems. - : SPRINGERNATURE. - 1875-6891 .- 1875-6883. ; 16:1
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Feature selection; Machine learning; Metaheuristic algorithms; Artificial ecosystem-based optimization; Dwarf mongoose optimization
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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