AI-Driven Distributed Control & Optimization Algorithms for Networked Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 173
Special Issue Editor
Interests: networked control systems; advanced sliding mode control; predefined-time stability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As networked systems have grown in complexity, advanced distributed strategies are urgently needed to further enhance system performance. In addition, the rapid evolution of artificial intelligence (AI) has unlocked novel avenues for designing distributed algorithms. Such innovations are poised to significantly enhance the autonomy, self-governance, and intelligence of networked systems. This Special Issue will specifically focus on AI-driven distributed control & optimization algorithms, addressing critical challenges around scalability, robustness, and adaptability, and thereby advancing the theoretical foundations and practical applications of next-generation networked systems. Contributions should integrate cutting-edge AI methodologies with distributed control & optimization frameworks to achieve breakthroughs in system efficiency and resilience.
Finally, we would like to thank Mr. Jing-Zhe Xu for his help in the creation of this Special Issue.
Dr. Chang-Duo Liang
Guest Editor
Manuscript Submission Information
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Keywords
- reinforcement learning based distributed control for networked systems
- neural network-based model predictive control for networked systems
- transfer learning based cross domain control for networked systems
- large language models based distributed control for networked systems
- AI-driven active exploration and path planning for networked systems
- AI-driven distributed task assignment for networked systems
- AI-driven optimal topology reconstruction for networked systems
- AI-driven distributed parameter optimization for networked systems
- evolutionary computation based multimodal optimization
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