Papers
3
Total Citations
33
H-Index
3
About
Gushen Jin is a computational intelligence researcher specializing in metaheuristic optimization algorithms, with a particular focus on enhancing physics-based optimization techniques for solving complex global optimization problems. His work centers on the Equilibrium Optimizer (EO) framework, where he has made significant contributions by developing innovative hybrid and adaptive variants to overcome fundamental limitations inherent in standard optimization algorithms. Jin's most notable contributions include pioneering a hybrid Equilibrium Optimizer integrated with Moth Flame Optimization, which has garnered 18 citations since its 2024 publication, demonstrating rapid uptake within the optimization community. His research systematically addresses critical challenges such as premature convergence, poor population diversity, and the delicate balance between exploration and exploitation — long-standing hurdles in swarm intelligence and evolutionary computation. His 2023 works introduced adaptive guided mechanisms with spiral search strategies and chaos-based adaptive techniques, collectively accumulating an additional 15 citations and reinforcing his reputation as a methodical innovator in algorithm design. By drawing inspiration from physical phenomena and incorporating sophisticated mathematical enhancements, Jin's research provides practical, computationally efficient tools applicable across engineering, machine learning, and scientific domains. His growing citation record reflects increasing recognition from the global optimization research community.
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