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

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

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第10期603-623,共21页计算机、材料和连续体（英文）

基　　金：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.

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 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.

关 键 词：OPTIMIZATION metaheuristics African bison optimization engineering problems 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]