Like Zhang
Papers
1
Total Citations
4
H-Index
1
About
Like Zhang is a pioneering researcher in the intersection of intelligent optimization algorithms and sustainable manufacturing systems. His work primarily focuses on multi-objective metaheuristic optimization, reinforcement learning, and energy-efficient production scheduling. Zhang’s most impactful contribution is the development of an improved multi-objective harmony search algorithm that integrates reinforcement learning to solve complex automated assembly line balancing problems while simultaneously minimizing energy consumption. This innovative approach, detailed in his 2025 paper, has already garnered 4 citations, signaling its growing influence in the field of green manufacturing and Industry 4.0. By bridging the gap between adaptive learning and classical optimization, Zhang’s research offers practical solutions for reducing carbon footprints in high-volume production environments. His work is particularly notable for its dual emphasis on computational efficiency and real-world applicability, making him a rising voice in the push toward sustainable automation. Zhang’s ongoing investigations promise to further transform how industries balance productivity with environmental responsibility.
Research Focus
Key Achievements
Top Papers
- 1