Dynamic opposite learning enhanced teaching–learning-based optimization

The teaching–learning-based optimization (TLBO) algorithm has been one of most popular bio-inspired meta-heuristic algorithms due to the competitive converging speed and high accuracy. A batch of TLBO variants has been proposed to enhance the exploitation ability and accelerate the exploration proce...

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Bibliographic Details
Published in:Knowledge-based systems 2020-01, Vol.188, p.104966, Article 104966
Main Authors: Xu, Yunlang, Yang, Zhile, Li, Xiaoping, Kang, Huazhou, Yang, Xiaofeng
Format: Article
Language:English
Subjects:
Algorithms
Asymmetry
Benchmarks
Convergence
Dynamic-opposite learning
Exploitation
Exploration
Global optimization
Heuristic methods
Machine learning
Mathematical analysis
Opposition-based learning
Optimization
Searching
Teaching–learning-based optimization
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Summary:The teaching–learning-based optimization (TLBO) algorithm has been one of most popular bio-inspired meta-heuristic algorithms due to the competitive converging speed and high accuracy. A batch of TLBO variants has been proposed to enhance the exploitation ability and accelerate the exploration process. However, they still suffer from premature convergence in solving complex non-linear problems. In the study, a novel TLBO variant named dynamic-opposite learning TLBO (DOLTLBO) is proposed, which employs a new dynamic-opposite learning (DOL) strategy to overcome premature convergence. The search space of DOL has the characteristics of asymmetry and dynamically adjusting along with a random opposite number. The asymmetric search space significantly increase the probability for the population in obtaining the global optimum, which holistically improves the exploitation capability of DOLTLBO. Meanwhile, the dynamically changing characteristic enriches the diversity of the search space, thus enhancing the exploration ability. To validate the proposed DOL operator and DOLTLBO algorithm, shifted and rotated benchmark functions from CEC 2014, multiextremal functions and constrained engineering problems have been experimented upon. Comprehensive numerical results with the comparisons with the state-of-the-art counterparts show that DOLTLBO has significant advantages of converging to the global optimum on most benchmarks and engineering problems, which also validates the superiority of the novel DOL operator.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.104966