Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm

  • Ali Arshaghi Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran
  • Mohsen Ashourian Department of Electrical Engineering, Majlesi Branch, Islamic Azad University
  • Leila Ghabeli Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Keywords: Buzzard Optimization Algorithm; Global optimization; benchmark dataset; bio inspired Meta-heuristic.


Various algorithms have been presented during the past decade to solve different complex optimization problems of science and industrial. The meta-heuristic algorithms have high noted among scientific and researchers. Hence, these algorithms have been progressed and outspread in the last years. Many of these algorithms are inspired from nature with various conditions. In this paper, a new algorithm based on initial population, the Buzzards Optimization Algorithm (BUZOA), is introduced. Special and marvelous lifestyle of buzzards and their competition characteristics for prey have been the basic motivation for initialized of this new optimization algorithm. Solution of this algorithm has been compared with the newest and well-known meta-heuristics and some benchmark problems, test functions. Results show the best performance of BUZOA algorithm compared to the other algorithms mentioned in this paper and runtime program is fast.


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How to Cite
Arshaghi, A., Ashourian, M., & Ghabeli, L. (2019). Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm. Majlesi Journal of Electrical Engineering, 13(3). Retrieved from