文献类型：期刊文献

英文题名：A parallel chimp optimization algorithm based on tracking-learning and fuzzy opposition-learning behaviors for data classification

作者：Lai, Zhaolin[1,2];Li, Guangyuan[1,2];Feng, Xiang[3];Hu, Xiaochun[1,2];Jiang, Caoqing[1,2]

机构：[1]Guangxi Univ Finance & Econ, Sch Big Data & Artificial Intelligence, Nanning 530003, Peoples R China;[2]Guangxi Key Lab Big Data Finance & Econ, Nanning 530003, Peoples R China;[3]East China Univ Sci & Technol, Dept Comp Sci, Shanghai 200237, Peoples R China

年份：2024

卷号：157

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20241515867737);WOS:【SCI-EXPANDED(收录号:WOS:001223118100001)】；

基金：Acknowledgments This work was supported by the Young and middle-aged teachers scientific research basic ability improvement project of Guangxi, China under Grant NO. 2023KY0678, Guangxi Key Laboratory of Big Data in Finance and Economics, China, and Guangxi First-class Discipline Statistics Construction Project Fund, China.

语种：英文

外文关键词：Global optimization - Learning algorithms - Parallel architectures

摘要：Chimp optimization algorithm (ChOA), which simulates the social behaviors of chimps, is a novel swarm intelligence algorithm for solving global optimization problems. ChOA has the advantages of fast convergence and avoiding falling into local optimum. However, the global search capability is weakened and the time overhead is too large when solving complex optimization problems. In order to improve the overall performance of ChOA, a parallel chimp optimization algorithm based on tracking-learning and fuzzy opposition-learning behaviors (PChOA) is proposed in this paper. First, a tracking-learning behavior is designed to improve the search accuracy. Second, a fuzzy opposition-learning behavior is adopted to enhance the global search capability. Third, a parallel computing architecture is developed to accelerate computational speed. Moreover, the convergence of our proposed PChOA has been analyzed theoretically. To validate the effectiveness of PChOA, it is applied to solve classification problem. The experimental results demonstrate that the classification performance of our proposed algorithm outperforms six other state of the art algorithms on most used datasets. Meanwhile, the time overhead of PChOA is significantly reduced in the environment of parallel computing. When the number of processors is increased to 16, PChOA costs less time than NBTree which is the fastest comparison algorithm in the experiment.