Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Optimization problems are prevalent across scientific and engineering disciplines.
• Existing metaheuristic algorithms often face challenges with local optima and convergence speed.
• There is a continuous need for novel, efficient optimization techniques.

Purpose of the Study:

• To introduce a new metaheuristic algorithm, the Greater Cane Rat Algorithm (GCRA).
• To model the intelligent foraging behaviors of greater cane rats for optimization.
• To evaluate GCRA's performance on diverse benchmark and engineering problems.

Main Methods:

• GCRA is developed based on the foraging and social behaviors of greater cane rats, including exploration and exploitation phases.
• The algorithm's efficacy is tested on 22 classical benchmark functions, 10 CEC 2020 complex functions, and CEC 2011 real-world problems.
• Performance is further validated using six engineering domain problems.

Main Results:

• GCRA demonstrated superior performance, achieving optimal or near-optimal solutions on tested functions.
• The algorithm effectively evaded local minima, outperforming ten state-of-the-art algorithms.
• Statistical analyses using Friedman and Wilcoxon signed rank tests confirm GCRA's efficacy and stability.

Conclusions:

• The Greater Cane Rat Algorithm (GCRA) is a promising new metaheuristic for complex optimization tasks.
• GCRA's biologically inspired approach offers advantages in solution quality and robustness.
• The algorithm's source code is publicly available for further research and application.