Open Access

ARTICLE

African Bison Optimization Algorithm: A New Bio-Inspired Optimizer with Engineering Applications

Jian Zhao^1,2,*, Kang Wang^1,2, Jiacun Wang^3,*, Xiwang Guo⁴, Liang Qi⁵

1 School of Science, University of Science and Technology Liaoning, Anshan, 114051, China
2 Team of Artificial Intelligence Theory and Application, University of Science and Technology Liaoning, Anshan, 114051, China
3 Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA
4 Information and Control Engineering College, Liaoning Petrochemical University, Fushun, 113000, China
5 Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao, 266590, China

* Corresponding Authors: Jian Zhao. Email: ; Jiacun Wang. Email:

(This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)

Computers, Materials & Continua 2024, 81(1), 603-623. https://doi.org/10.32604/cmc.2024.050523

Received 08 February 2024; Accepted 21 August 2024; Issue published 15 October 2024

Abstract

This paper introduces the African Bison Optimization (ABO) algorithm, which is based on biological population. ABO is inspired by the survival behaviors of the African bison, including foraging, bathing, jousting, mating, and eliminating. The foraging behavior prompts the bison to seek a richer food source for survival. When bison find a food source, they stick around for a while by bathing behavior. The jousting behavior makes bison stand out in the population, then the winner gets the chance to produce offspring in the mating behavior. The eliminating behavior causes the old or injured bison to be weeded out from the herd, thus maintaining the excellent individuals. The above behaviors are translated into ABO by mathematical modeling. To assess the reliability and performance of ABO, it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with constraints. The findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

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Keywords

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