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About

Gaurav Dhiman is a prolific researcher working at the intersection of computational intelligence, metaheuristic optimization, and artificial intelligence. His scholarship spans swarm intelligence, nature-inspired algorithms, and the practical application of AI technologies to real-world challenges. Among his most celebrated contributions is a landmark retrospective on Particle Swarm Optimization, amassing over 220 citations, which critically surveys 25 years of advancements in one of computing's most enduring optimization paradigms. Dhiman has also made significant strides in developing and refining physics-based metaheuristic methods, particularly through multiple enhanced variants of the Equilibrium Optimizer, addressing persistent limitations such as local optima stagnation and poor population diversity. His timely work exploring robotics, machine learning, and AI during the COVID-19 pandemic demonstrated his commitment to applying computational methods to urgent humanitarian problems. More recently, his in-depth survey of ChatGPT and large language models has already attracted substantial attention, reflecting his ability to engage with rapidly evolving AI frontiers. Through his adaptive algorithms — including improved salp swarm and equilibrium optimizer variants — Dhiman consistently advances the theoretical rigor and practical performance of optimization techniques, making him a valuable voice in the global computational intelligence community.