Yazar "Kaur, Amandeep" 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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    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.