Integrating Chaos for Enhanced Stability and Exploitation in Gorilla Troops Optimization

Optimization techniques are widely used for their simplicity and robust performance, making them a better choice for solving complex problems across various domains. Among these techniques, metaheuristic algorithms have shown great promise due to their flexibility and ability to handle diverse optimization challenges. This study introduces a Chaotic Gorilla Troops Optimizer (CGTO), which enhances the Gorilla Troops Optimizer (GTO) based on the Logistic map to improve its search capabilities. While GTO is a promising algorithm, it suffers from certain limitations, such as restricted global search capability, slow convergence speed, and a tendency to fall into local optima when addressing complex optimization problems. To address these problems, initially, the proposed CGTO incorporates enhancements designed to improve convergence behavior and exploitability, making it more effective for a broad range of optimization tasks. The CGTO achieves these improvements by modifying the position update mechanism of the GTO algorithm through the integration of chaotic maps. Secondly, the performance of the e algorithm's performance is with PSO and the original GTO algorithm. Then the CGTO is verified through, unimodal, multimodal, and fixed-dimensional multimodal functions which shows that CGTO consistently outperformed its counterparts in most cases. Finally, the obtained result shows CGTO's superiority in terms of solution quality and convergence rate, establishing its effectiveness as a robust optimization technique for solving complex problems. These findings demonstrate the potential of CGTO to address the limitations of traditional algorithms while delivering reliable and efficient optimization performance.