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A modified teaching-learning-based optimization algorithm for numerical function optimization

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第6期10卷 1357-1371页

作者： Niu, Peifeng Ma, Yunpeng Yan, Shanshan Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Hydropower Stn Adm Taolinkou Reservoir Qinhuangdao 066004 Hebei Peoples R China

In this paper, a kind of modified teaching-learning-based optimization algorithm (MTLBO) is proposed to enhance the solution quality and accelerate the convergence speed of the conventional TLBO. Compared with TLBO, the MTLBO algorithm possesses different updating mechanisms of the individual solution. In teacher phase of the MTLBO, the students are divided into two groups according to the mean result of learners in all subjects. Moreover, the two groups present different updating strategies of the solution. In learner phase, the students are still divided into two groups, where the first group includes the top half of the students and the second group contains the remaining students. The first group members increase their knowledge through interaction among themselves and study independently. The second group members increase their marks relying on their teacher. According to the above-mentioned updating mechanisms, the MTLBO can provide a good balance between the exploratory and exploitative capabilities. Performance of the proposed MTLBO algorithm is evaluated by 23 unconstrained numerical functions and 28 CEC2017 benchmark functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed.

关键词： Teaching-learning-based optimization Modified teaching-learning-based optimization Exploratory and exploitative capabilities unconstrained numerical functions CEC2017

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