Abstract
Spectrum sensing based on Beamforming, like others classification problem, require feature selection to perform learning algorithms and enhance the classification task. This paper proposes a novel version of the Dingo Optimization Algorithm (DOA) to optimize feature selection for a Deep Neural Network (DNN) classifier. Two improvements are introduced to avoid the premature convergence problem and stagnation in the local optima of the original DOA. First, the chaos strategy is executed to produce a high level of diversification in the algorithm, which improves its ability to escape from potential local optimums. Second, the weight factor is introduced to boot up the search process to the global optima. Here, the aim is to improve the DOA for feature selection in the deep learning approach in order to enhance the performance of blind spectrum sensing based on Beamforming in the context of cognitive radio (CR). Through simulations results, we illustrate that our algorithm, called Chaotic Dingo Optimization Algorithm (CDOA), outperforms the original one and a set of state-of-the-art optimization algorithms (i.e., HS, BBO, PSO, and SA) for feature selection in the learning approach.
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Ben Chaabane, S., Bouallegue, K., Belazi, A., Kharbech, S., Bouallegue, A. (2022). Chaotic Dingo Optimization Algorithm: Application in Feature Selection for Beamforming Aided Spectrum Sensing. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_52
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