期刊
KNOWLEDGE-BASED SYSTEMS
卷 188, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2019.104966
关键词
Teaching-learning-based optimization; Dynamic-opposite learning; Opposition-based learning; Global optimization
资金
- National Science and Technology Major Project of China [2017ZX02101007]
- Jihua Laboratory Foundation of Guangdong Province Laboratory [18002U1Z00]
- Science and Technology Commission of Shanghai Municipality [18DZ1112600]
- National Natural Science Foundation (NNSF) of China [61274109]
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. (C) 2019 The Authors. Published by Elsevier B.V.
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