A Hybrid Nonlinear Greater Cane Rat Algorithm with Teaching-Learning-Based Optimization for Global Optimization and Constrained Engineering Applications | JoVE Visualize

Area of Science:

Computational Intelligence
Swarm Intelligence Algorithms
Optimization Techniques

Background:

The Greater Cane Rat Algorithm (GCRA) is a swarm intelligence paradigm inspired by GCR survival behaviors.
The original GCRA exhibits limitations in high-dimensional problems, including premature convergence and local optima stagnation.
These limitations restrict the practical application of GCRA in complex engineering optimization.

Purpose of the Study:

To introduce an enhanced hybrid variant of the GCRA, termed TLGCRA, by integrating Teaching-and-Learning-Based Optimization (TLBO).
To address the inherent defects of the original GCRA, improving its performance in complex and high-dimensional optimization scenarios.
To enhance convergence speed, solution precision, and algorithmic robustness for engineering applications.

Main Methods:

Integration of TLBO's two-stage teacher-student learning mechanism into the GCRA framework.
Implementation of an adaptive parameter tuning strategy to balance exploration and exploitation.
Extensive computational simulations on 23 benchmark functions and 6 constrained engineering design problems.
Benchmarking against canonical GCRA, LPSO, and ten other metaheuristic approaches.

Main Results:

The TLGCRA demonstrated significant performance advantages over existing algorithms in convergence velocity, solution precision, and resilience.
Marked improvement in optimal solution precision for complex multimodal functions was observed.
Near-zero standard deviation across multiple runs in engineering cases confirmed TLGCRA's excellent stability.
Statistical tests (Friedman, Wilcoxon signed-rank) validated the algorithm's robust optimization efficacy.

Conclusions:

The proposed TLGCRA effectively overcomes the limitations of the original GCRA, offering enhanced environmental adaptability and comprehensive optimization performance.
The hybrid algorithm achieves faster convergence, higher computational accuracy, and outstanding stability and robustness.
TLGCRA provides a viable and effective framework for tackling intricate constrained engineering optimization challenges.