A Rival Gorilla Troops Optimizer for solving Global Optimization problems

Abstract

The Gorilla Troops Optimization Algorithm (GTOA) is a population-based optimization algorithm that was introduced in 2021. It is inspired by the behavior of gorillas, such as exploring known and new areas, following others, and competing. However, like other metaheuristic algorithms, GTOA has challenges with local optima, diversity, and uneven usage. To address these issues, a modified version named the Rival GTO Algorithm (RGTOA) was proposed. RGTOA reduces the number of optimization operators and adopts a single exploitation mechanism to represent the competition among gorillas. The RGTOA also enhances exploration using a damping factor. The optimal parameters for RGTOA were determined using the Taguchi method. RGTOA was tested against 68 benchmark functions and compared with state-of-the-art algorithms such as HBO, HHO, MFO, WOA, GWO, and GTOA. The results showed that RGTOA outperforms the original GTOA and other algorithms in terms of convergence speed and solution quality, making it a better option for solving complex problems.