G
Ghanshyam G. Tejani
Researcher at Goddard Space Flight Center
Publications - 33
Citations - 1208
Ghanshyam G. Tejani is an academic researcher from Goddard Space Flight Center. The author has contributed to research in topics: Optimization problem & Metaheuristic. The author has an hindex of 14, co-authored 26 publications receiving 691 citations. Previous affiliations of Ghanshyam G. Tejani include RK University & Pandit Deendayal Petroleum University.
Papers
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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.
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Truss optimization with natural frequency bounds using improved symbiotic organisms search
TL;DR: This work proposes an improved version of a recently proposed Symbiotic Organisms Search (SOS) called an Improved SOS (ISOS) to tackle the above-mentioned challenges and shows that ISOS is more reliable and efficient as compared to the basis SOS algorithm and other state-of-the-art algorithms.
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Structural optimization using multi-objective modified adaptive symbiotic organisms search
TL;DR: The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase with a normal line method as an archiving technique provide superior and competitive results than the former obtained results.
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Multiobjective adaptive symbiotic organisms search for truss optimization problems
TL;DR: A multiobjective adaptive symbiotic organisms search (MOASOS) and its two-archive technique for solving truss optimization problems and it is demonstrated that adaptive control is able to provide a better and competitive solutions when compared against the previous studies.
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Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics
TL;DR: This study compares the performance of four improved metaheuristics (viz.