Yazar "Singh, Krishna Kant" seçeneğine göre listele

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    BEPO: A novel binary emperor penguin optimizer for automatic feature selection
    (Elsevier, 2021) Dhiman, Gaurav; Oliva, Diego; Kaur, Amandeep; Singh, Krishna Kant; Vimal, S.; Sharma, Ashutosh; Cengiz, Korhan
    Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin's huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm. (C) 2020 Elsevier B.V. All rights reserved.
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    Cooperative spectrum sensing optimization for cognitive radio in 6 G networks
    (Pergamon-Elsevier Science Ltd, 2021) Singh, Krishna Kant; Yadav, Piyush; Singh, Akansha; Dhiman, Gaurav; Cengiz, Korhan
    The upcoming sixth generation (6 G) systems can meet the high user demands. The existing communication systems are becoming inefficient in meeting the user demands. The multifold growth in the usage of high-definition multimedia applications requires new capabilities. The users are looking forward to high throughput and low latency. The shift from 5 G to 6 G networks is since the 6 G networks are expected to combine the terrestrial, aerial, and maritime communications into a robust network. This will provide the users a faster network with high reliability, accommodation to a larger number of users, and ultra-low latency. However, the limited availability of spectrum is a bottleneck in enhancing the user experience. Therefore, advanced techniques like cognitive radios and cooperative spectrum sensing are critical in the design of future network. The optimal usage and management of the available spectrum is significant for the performance of the network. In this paper, a cooperative spectrum sensing technique using Manta Ray Foraging Algorithm (MRFO) is proposed. The weighting vector at the fusion center is optimized using MRFO. The allocation of the spectrum is done using the optimal weight vector for secondary users. The proposed work aims at finding the maximum probability of detection. Probability of detection is significant in spectrum sensing. The channel needs to be sensed for the presence or absence of primary users. If the detection probability is maximized, then the channel usage efficiency will increase. The proposed method is compared with other state of the art methods. The results show that MRFO can be used efficiently for spectrum sharing by cognitive radios.
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    MOSOA: A new multi-objective seagull optimization algorithm
    (Pergamon-Elsevier Science Ltd, 2021) Dhiman, Gaurav; Singh, Krishna Kant; Soni, Mukesh; Nagar, Atulya; Dehghani, Mohammad; Slowik, Adam; Kaur, Amandeep
    This study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.