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Updated: May 28, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
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Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm and Its Application.

Zihao Cheng1, Li Cao2, Yang Qiu2

  • 1College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
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This study introduces the Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm (CEGCRA) to improve optimization accuracy and speed. The enhanced algorithm demonstrates superior performance in solving complex engineering problems compared to existing methods.

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • The Greater Cane Rat Algorithm (GCRA) faces challenges with population distribution, local optima, and balancing exploration/exploitation.
  • Existing optimization algorithms often struggle with premature convergence and insufficient accuracy.

Purpose of the Study:

  • To propose a Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm (CEGCRA) addressing GCRA's limitations.
  • To enhance population diversity, optimize exploration-exploitation balance, and improve convergence speed and accuracy.

Main Methods:

  • Utilizing a piecewise chaotic map for improved initial population distribution and diversity.
  • Implementing an accumulated difference foraging strategy for adaptive search direction and step size adjustment.
Keywords:
accumulated differencechaotic mapengineering optimizationglobal optimizationgreater cane rat algorithm

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  • Refining the exploration-exploitation switching mechanism and boundary constraint handling.

    • Main Results:

    • CEGCRA achieved an average 35.3% reduction in optimal fitness value and 22.7% in standard deviation compared to GCRA.
    • Convergence speed increased by an average of 28.9% on benchmark test suites.
    • CEGCRA outperformed GCRA, PSO, DE, and SSA in optimization accuracy, convergence speed, robustness, and constraint handling for engineering problems.

    Conclusions:

    • The CEGCRA effectively overcomes the limitations of the original GCRA, offering enhanced performance.
    • The algorithm demonstrates significant improvements in accuracy, speed, and robustness for complex, high-dimensional, and constrained optimization problems.
    • CEGCRA shows strong potential for solving real-world engineering design challenges.