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

By Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili. International Journal of Intelligent Systems, 2021

933 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1ba7e7f27189105fa7cefddb007bb47274ac65e6

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In Artificial Intelligence

  1. Mohammad Namazi
  2. Laya Ebrahimi
  3. Benyamin Abdollahzadeh
  4. Farhad Soleimanian Gharehchopogh

In Computational Theory and Mathematics

  1. Mohammad Namazi
  2. Laya Ebrahimi
  3. Benyamin Abdollahzadeh
  4. Farhad Soleimanian Gharehchopogh

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