Zhikuan Zhu

Papers

Total Citations

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About

Zhikuan Zhu is a rising researcher in the field of intelligent computing and real-time systems, with a primary focus on deep reinforcement learning (DRL) and optimization algorithms for task scheduling. His most-cited work, "A DRL-based online real-time task scheduling method with ISSA strategy" (2024), has already garnered 7 citations, signaling early impact in a niche yet critical area of cyber-physical systems. Zhu’s key contribution lies in integrating DRL with an improved sparrow search algorithm (ISSA) to dynamically allocate computational resources, addressing the challenge of latency-sensitive tasks in edge and cloud environments. This hybrid approach not only enhances scheduling efficiency but also adapts to real-time workload fluctuations, offering a scalable solution for modern distributed systems. By bridging theoretical DRL models with practical scheduling constraints, Zhu’s work paves the way for more autonomous and responsive computing architectures. His research is particularly relevant for students and engineers exploring AI-driven automation in IoT, smart manufacturing, and autonomous vehicles. As his citation count grows, Zhu is establishing himself as a promising voice in the intersection of reinforcement learning and operational optimization.