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Artificial gorilla troops optimizer

version 1.0.0 (3.43 KB) by benyamin abdollahzadeh
Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems

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Updated 15 Jul 2021

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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.
Main paper:
Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili,International Journal of Intelligent Systems,2021,DOI: https://doi.org/10.1002/int.22535.
Download the paper from:
https://www.researchgate.net/publication/353186350_Artificial_gorilla_troops_optimizer_A_new_nature-inspired_metaheuristic_algorithm_for_global_optimization_problems
https://onlinelibrary.wiley.com/doi/10.1002/int.22535
Email: benyamin.abdolahzade@gmail.com
Homepage:https://www.researchgate.net/profile/Benyamin-Abdollahzadeh

Cite As

Abdollahzadeh, B, Soleimanian Gharehchopogh, F, Mirjalili, S. Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst. 2021; 1- 72. https://doi.org/10.1002/int.22535

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