S
Sankalap Arora
Researcher at DAV University
Publications - 31
Citations - 3115
Sankalap Arora is an academic researcher from DAV University. The author has contributed to research in topics: Firefly algorithm & Metaheuristic. The author has an hindex of 17, co-authored 31 publications receiving 1712 citations. Previous affiliations of Sankalap Arora include Punjab Technical University.
Papers
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Journal ArticleDOI
Butterfly optimization algorithm: a novel approach for global optimization
Sankalap Arora,Satvir Singh +1 more
TL;DR: A new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems and results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.
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Chaotic whale optimization algorithm
Gaganpreet Kaur,Sankalap Arora +1 more
TL;DR: Chaos theory is introduced into WOA optimization process to enhance the global convergence speed and to get better performance, and the results prove that the chaotic maps are able to improve the performance of WOA.
Journal ArticleDOI
Chaotic grey wolf optimization algorithm for constrained optimization problems
Mehak Kohli,Sankalap Arora +1 more
TL;DR: This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed, and shows that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.
Journal ArticleDOI
Binary butterfly optimization approaches for feature selection
Sankalap Arora,Priyanka Anand +1 more
TL;DR: Experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.
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Chaotic grasshopper optimization algorithm for global optimization
Sankalap Arora,Priyanka Anand +1 more
TL;DR: The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process as mentioned in this paper, and the results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.