Parameter Exploration in the Artificial Gorilla Troops Optimizer Algorithm

The constant evolution in the development of bio-inspired algorithms drives us to continuously investigate and explore this vast and exciting area of study, with the aim of understanding which types of problems can be better addressed. This study presents an analysis of the parameters of the Artificial Gorilla Troops Optimizer algorithm, which controls exploration and exploitation in optimization. The examined parameters are p, W, and Beta, which were subjected to variations in their values and evaluated on six unimodal Benchmark functions. It was found that p produces better results with low values, while W significantly improves results with higher values. For “Beta,” improvements were observed with low values in some functions and with higher values in others. Furthermore, this opens up opportunities for future research, including dynamic parameter tuning and the application of the Artificial Gorilla Troops Optimizer in a wide range of fields, harnessing its potential for effectively solving complex problems.