Amarjeet Singh

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About

Amarjeet Singh is a researcher whose work centers on computational intelligence and nature-inspired optimization algorithms. His most recognized contribution lies in the development and refinement of swarm-based metaheuristic techniques, particularly his 2015 study introducing new variants of the Glowworm Swarm Optimization (GSO) algorithm with modified step size strategies. This work addressed key limitations in the original GSO framework, proposing enhancements that improve convergence behavior and solution quality in complex optimization landscapes. By systematically varying step size parameters, Singh demonstrated meaningful gains in algorithmic performance, contributing a practical and reproducible advance to the field of evolutionary computation. While his citation record is still developing, with his prominent work having garnered 9 citations, his research speaks to a growing community of scholars interested in bio-inspired problem-solving methods applicable across engineering, networking, and artificial intelligence domains. For students and researchers exploring swarm intelligence, Singh's contributions offer a thoughtful entry point into understanding how fine-tuning fundamental algorithmic parameters can yield significant improvements in optimization efficiency and robustness.