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Updated: May 10, 2025

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FROM: A Fish Recognition-Inspired Optimization Method for Multi-Agent Decision-Making Problems with a Fluid

Yuchen Wang1, Lei Sun1

  • 1School of Software, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

Biomimetics (Basel, Switzerland)
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

A new fish recognition-inspired optimization method (FROM) enhances underwater multi-agent systems by improving neighbor topology and learning strategies. This approach effectively addresses hydrodynamic constraints in dynamic fluid environments.

Keywords:
bio-inspired optimization methodfish recognitionhydrodynamic analysismulti-agent learning

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Area of Science:

  • Robotics and Control Systems
  • Fluid Dynamics
  • Artificial Intelligence

Background:

  • Underwater multi-agent systems (UMAS) are hindered by hydrodynamic constraints, degrading conventional optimization algorithm performance.
  • Dynamic fluid environments pose significant challenges for UMAS decision-making and coordination.
  • Existing optimization methods struggle to adapt to the complex hydrodynamic interactions in underwater settings.

Purpose of the Study:

  • To introduce a novel optimization method inspired by fish recognition for UMAS operating in fluid environments.
  • To address the performance degradation caused by hydrodynamic constraints in UMAS.
  • To enhance the decision-making capabilities of UMAS in dynamic underwater conditions.

Main Methods:

  • Developed a Fish Recognition-inspired Optimization Method (FROM) incorporating fish recognition characteristics.
  • Implemented a neighbor topology improvement strategy based on vision recognition.
  • Introduced a learning strategies improvement based on hydrodynamic recognition.
  • Analyzed the computational complexity of the FROM algorithm.

Main Results:

  • The FROM algorithm demonstrated superior performance compared to existing methods.
  • Statistical analysis showed significant improvements in minimum, maximum, standard deviation, mean, and median objective function values.
  • The computational complexity of FROM was found to be acceptable for practical applications.

Conclusions:

  • The proposed FROM algorithm offers a robust solution for multi-agent decision-making problems under fluid environment constraints.
  • FROM effectively overcomes the limitations of conventional algorithms in dynamic hydrodynamic conditions.
  • The fish recognition-inspired approach provides a promising direction for future UMAS research.