V
Vimal Savsani
Researcher at Pandit Deendayal Petroleum University
Publications - 84
Citations - 7463
Vimal Savsani is an academic researcher from Pandit Deendayal Petroleum University. The author has contributed to research in topics: Optimization problem & Metaheuristic. The author has an hindex of 26, co-authored 82 publications receiving 5461 citations. Previous affiliations of Vimal Savsani include Sardar Vallabhbhai National Institute of Technology, Surat & Canadore College.
Papers
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Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems
TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
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Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems
TL;DR: An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions.
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Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems
R. V. Rao,Vimal Savsani,J. Balic +2 more
TL;DR: An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems and the results show the better performance of the proposed algorithm.
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Heat transfer search (HTS)
Vivek Patel,Vimal Savsani +1 more
TL;DR: A novel meta-heuristic optimization method based on the law of thermodynamics and heat transfer for solving constraint optimization problems and the results obtained are compared with some well-known metaheuristic search algorithms available in the literature.
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Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization
TL;DR: Three modified versions of the symbiotic organisms search algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency and reveal that the adaptive SOS algorithm is more reliable and efficient than thebasic SOS algorithm and other state-of-the-art algorithms.