Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems

TLDR

Metaheuristics are essential for solving optimization problems and are largely inspired by the collective intelligence of natural organisms. This paper introduces the Artificial Gorilla Troops Optimizer (GTO), a new metaheuristic algorithm inspired by gorilla troop social intelligence. The GTO mathematically models gorilla troop dynamics, employs novel exploration and exploitation mechanisms, and is evaluated on 52 benchmark functions and seven engineering problems using Friedman's and Wilcoxon rank‑sum tests against existing metaheuristics. Results show that the GTO outperforms comparative algorithms on most benchmark functions, especially high‑dimensional ones, and delivers superior performance compared to other metaheuristics.

Abstract

Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.