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An Improved Greater Cane Rat Algorithm with Adaptive and Global-Guided Mechanisms for Solving Real-World Engineering

Yepei Chen1, Zhangzhi Tian1, Kaifan Zhang1

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

Biomimetics (Basel, Switzerland)
|September 26, 2025
PubMed
Summary

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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An improved adaptive and global-guided greater cane rat algorithm (AGG-GCRA) enhances optimization speed and precision. This novel algorithm overcomes limitations of the original GCRA, offering superior performance in complex engineering problems.

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The original Greater Cane Rat Algorithm (GCRA) exhibits intelligent exploration but suffers from premature convergence and insufficient local exploitation in complex optimization tasks.
  • Existing metaheuristic algorithms often struggle with balancing exploration and exploitation, leading to suboptimal solutions for intricate problems.

Purpose of the Study:

  • To introduce an enhanced variant, the Adaptive and Global-Guided Greater Cane Rat Algorithm (AGG-GCRA), designed to improve convergence speed, solution precision, and stability.
  • To address the limitations of the standard GCRA by incorporating mechanisms for global guidance, flexible parameter adjustment, solution preservation, and local optima escape.

Main Methods:

  • Developed AGG-GCRA by integrating four key improvements: global optimum guidance, a flexible parameter adjustment system, a top-quality solution retention mechanism, and a local perturbation mechanism.
Keywords:
adaptive and global-guided mechanismsgreater cane rat algorithmsolving optimization problems

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  • Conducted 20 experiments on 26 standard benchmark functions and six real-world engineering optimization problems.
  • Compared AGG-GCRA against 11 advanced metaheuristic optimization methods using statistical tests like Friedman ranking and Wilcoxon signed-rank tests.

    • Main Results:

    • AGG-GCRA demonstrated superior performance over competing algorithms in convergence rate, solution precision, and robustness.
    • The algorithm consistently found the global optimal solution across multiple runs for five engineering cases, showing excellent repeatability with a standard deviation near zero.
    • Statistical analyses confirmed the significant effectiveness and importance of the proposed AGG-GCRA approach.

    Conclusions:

    • AGG-GCRA offers a more efficient and stable intelligent optimization tool compared to existing methods.
    • The enhancements successfully mitigate the limitations of the original GCRA, providing a robust solution for diverse optimization challenges.
    • The study validates AGG-GCRA's capability for high-quality global search and reliable performance in complex optimization scenarios.