Adaptive control and optimization of multi-agent networks

Nasim Nezamoddini, Hiroki Sayama

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This research proposes a novel technique for distributed control and optimization of the networked systems considering the uncertainties associated with internal complex dynamics and external interactions with the environment. The proposed technique applies a distributed multi-agent framework that minimizes the overall objective of the system subject to the limitations on the shared resources. In this framework, each agent tries to optimize its decisions and improve the learning strategy based on artificial neural networks (ANN) without having access to the statistical distributions of the involved parameters. Comprehensive experiments are implemented to investigate the effects of the learning mechanism and the level of uncertainties. The efficiency of the technique is tested by comparing the proposed technique with the existing traditional network optimization techniques. The proposed technique can be utilized in a variety of applications such as min cost flow problems, disease propagation models, and distributed controls over man-made networks such as supply chain and power grid.

Original languageEnglish
Title of host publicationProceedings of the 2020 IISE Annual Conference
EditorsL. Cromarty, R. Shirwaiker, P. Wang
PublisherInstitute of Industrial and Systems Engineers, IISE
Number of pages6
ISBN (Electronic)9781713827818
Publication statusPublished - 2020
Externally publishedYes
Event2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020 - Virtual, Online, United States
Duration: 2020 Nov 12020 Nov 3

Publication series

NameProceedings of the 2020 IISE Annual Conference


Conference2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020
Country/TerritoryUnited States
CityVirtual, Online


  • Adaptive Control
  • Multi-agent Systems
  • Network Optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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