Advances in teaching-learning-based optimization algorithm: A comprehensive survey(ICIC2022)

被引：28

作者：

Zhou, Guo ^{[1
]}

Zhou, Yongquan ^{[2
,3
,5
]}

Deng, Wu ^{[4
]}

Yin, Shihong ^{[2
]}

Zhang, Yunhui ^{[2
]}

机构：

[1] China Univ Polit Sci & Law, Dept Sci & Technol Teaching, Beijing 102249, Peoples R China

[2] Guangxi Univ Nationalities, Coll Articial Intelligence, Nanning 530006, Peoples R China

[3] Gunagxi Univ Nationalities, Xiangsihu Coll, Nanning 532100, Guangxi, Peoples R China

[4] Civil Aviat Univ China, Sch Elect Informat & Automat, Tianjin 300300, Peoples R China

[5] Guangxi Key Labs Hybrid Computat & IC Design Anal, Nanning 530006, Peoples R China

来源：

NEUROCOMPUTING | 2023年 / 561卷

关键词：

Metaheuristic; Optimization; Optimization problem; Population-based; Teaching-learning-based optimization algorithm; PROBABILISTIC NEURAL-NETWORKS; MULTIOBJECTIVE OPTIMIZATION; PARAMETER OPTIMIZATION; DISPATCH PROBLEM; TLBO ALGORITHM; OPTIMAL-DESIGN; FLOW-SHOP; CONSTRAINTS; SEGMENTATION; METHODOLOGY;

D O I：

10.1016/j.neucom.2023.126898

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页数：20

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