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

•Teaching-learning-based optimization (TLBO) is one of population-based heuristic stochastic swarm intelligent algorithm.•In this paper, a comprehensive survey on the recent advances in TLBO is presented.•Literature survey reveals some interesting challenges and future research directions. Teaching-...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 561; S. 126898
Hauptverfasser: Zhou, Guo, Zhou, Yongquan, Deng, Wu, Yin, Shihong, Zhang, Yunhui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 07.12.2023
Schlagworte:
Metaheuristic
Optimization
Optimization problem
Population-based
Teaching–learning-based optimization algorithm
Teaching–learning-based optimization algorithm
Metaheuristic
Population-based
Optimization
Optimization problem
ISSN:0925-2312
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Zusammenfassung:•Teaching-learning-based optimization (TLBO) is one of population-based heuristic stochastic swarm intelligent algorithm.•In this paper, a comprehensive survey on the recent advances in TLBO is presented.•Literature survey reveals some interesting challenges and future research directions. 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.
ISSN:0925-2312
DOI:10.1016/j.neucom.2023.126898