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Distributed real-time dynamic cooperative optimization with unknown performance function form under resources

Zilun Hu1, Chao Ni1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu Province, China.

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|August 7, 2025
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Summary
This summary is machine-generated.

This study introduces a novel distributed dynamic optimization algorithm for real-time resource allocation in continuous time systems. It adaptively optimizes performance even when the system

Keywords:
Distributed optimizationReal-time collaborationResource constraintUnknown performance function

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Area of Science:

  • Control Systems Engineering
  • Optimization Theory
  • Distributed Computing

Background:

  • Continuous time systems often involve complex performance functions that are partially unknown.
  • Traditional resource allocation methods struggle in dynamic, time-varying environments.
  • Distributed systems require efficient coordination under resource constraints.

Purpose of the Study:

  • To design a distributed dynamic optimization algorithm for continuous time systems.
  • To develop an optimal resource allocation scheme for multi-node systems.
  • To address the challenge of optimizing unknown performance function components.

Main Methods:

  • A novel distributed dynamic optimization algorithm is proposed.
  • The algorithm operates in real-time using distributed information.
  • It employs adaptive strategies to handle unknown system dynamics.

Main Results:

  • The algorithm provides real-time optimal scheduling results in dynamic environments.
  • It successfully optimizes global performance adaptively, even with unknown performance functions.
  • Demonstrates effective resource allocation under constraints in multi-node systems.

Conclusions:

  • The developed algorithm offers a robust solution for real-time dynamic optimization in continuous time systems.
  • It enhances resource allocation efficiency in distributed environments.
  • The adaptive nature allows optimization without complete knowledge of system performance functions.