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Communication Dans Un Congrès Année : 2022

A hybrid Modified Black Widow Optimization and PSO Algorithm: Application in Feature Selection for Cognitive Radio Networks

Résumé

In spectrum sensing issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature selection. This paper proposes a new hybrid optimization algorithm to optimize feature selection for a Deep Neural Network (DNN) classifier. To surpass the premature convergence problem and improve the exploitation ability of the original Black Widow Optimization Algorithm (BWO), we mix a modified version of BWO and Particle Swarm Optimization (PSO), called MBWPSO. The aim is to enhance the performance of a blind spectrum sensing approach in the context of cognitive radio (CR) for wireless communications. Computer simulations show that the MBWPSO algorithm outperforms the original one and a set of state-of-the-art algorithms (i.e., HS, BBO, PSO, and SA) algorithms. The MBWPSO also exhibits the best performance once applied for feature selection in the above context
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Dates et versions

hal-04010917 , version 1 (02-03-2023)

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Sarra Ben Chaabane, Kais Boualleguet, Akram Belazi, Sofiane Kharbech, Ammar Bouallegue. A hybrid Modified Black Widow Optimization and PSO Algorithm: Application in Feature Selection for Cognitive Radio Networks. 27th Asia-Pacific Conference on Communications (APCC) - Creating Innovative Communication Technologies for Post-Pandemic Era, Oct 2022, Jeju Island, South Korea. ⟨10.1109/APCC55198.2022.9943598⟩. ⟨hal-04010917⟩
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