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AI-Driven Distributed Control & Optimization Algorithms for Networked Systems

This special issue belongs to the section “Evolutionary Algorithms and Machine Learning“.

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Algorithms - ISSN 1999-4893
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