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

作     者：Zhao, Jian Wang, Kang Wang, Jiacun Guo, Xiwang Qi, Liang 

作者机构：School of Science University of Science and Technology Liaoning Anshan 114051 China Team of Artificial Intelligence Theory and Application University of Science and Technology Liaoning Anshan 114051 China Department of Computer Science and Software Engineering Monmouth University West Long Branch NJ 07764 United States Information and Control Engineering College Liaoning Petrochemical University Fushun 113000 China Department of Computer Science and Technology Shandong University of Science and Technology Qingdao 266590 China 

出 版 物：《Computers, Materials and Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第1期

页      面：603-623页

核心收录：

学科分类：0828[工学-农业工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the National Natural Science Foundation of China (GrantNo. U1731128) theNatural Science Foundation of Liaoning Province (Grant No. 2019-MS-174) the Foundation of Liaoning Province EducationAdministration (GrantNo. LJKZ0279) the Team of Artificial Intelligence Theory and Application for the financial support.Funding Statement: This work was supported in part by the National Natural Science Foundation of China (Grant No. U1731128) the Natural Science Foundation of Liaoning Province (Grant No. 2019-MS-174) the Foundation of Liaoning Province Education Administration (Grant No. LJKZ0279) the Team of Artificial Intelligence Theory and Application for the financial support 

主　　题：Livestock 

摘      要：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 *** 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, thusmaintaining 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. © 2024 The Authors.