计算机科学
群体行为
启发式
进化计算
人工智能
过程(计算)
计算
机器学习
进化算法
算法
数学优化
数学
操作系统
作者
Guo Zhou,Yongquan Zhou,Wu Deng,Shihong Yin,Yunhui Zhang
出处
期刊:Neurocomputing
[Elsevier BV]
日期:2023-10-05
卷期号:561: 126898-126898
被引量:28
标识
DOI:10.1016/j.neucom.2023.126898
摘要
Teaching-learning-based optimization (TLBO) algorithm which imitates the teaching-learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of teaching-learning-based optimization is divided into two phases and each phase executes iterative learning operation. In this paper, we present a comprehensive survey on the recent advances in TLBO. A review of the current literature reveals intriguing challenges and suggests potential future research directions.
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