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- W2569793486 abstract "By mimicking the learning process of human in real-life, teaching-learning-based optimization algorithm (TLBO) is proposed for global optimization. Since then, it has been widely and effectively used in kinds of fields. As a swarm intelligent optimization method, TLBO has the virtue of fewer algorithm-parameters adjusting, easy to implement, and good convergence. However, there exist no advising directions in the learning phase, which may result in a decrease of local search ability of the TLBO when solving complex problems. In this paper, a collaborative learning model (CLM) which modified learner phase and novel self-studying phase is proposed to enhance both the global and local searching ability. In CLM method, a collaborative pattern or competitive pattern is probability chosen by learners in the learner phase. To efficiently conduct learners, in the self-studying phase, teacher updates his/her position according to neighborhood information adaptively. We perform the CLM method on a series of real-world resource allocation problem in multi-cell networks. Experimental results indicate that the CLM method is able to achieve more satisfactory or at least comparable solutions on most real-world problems." @default.
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- W2569793486 date "2016-01-01" @default.
- W2569793486 modified "2023-10-15" @default.
- W2569793486 title "A Collaborative Learning Model in Teaching-Learning-Based Optimization: Some Numerical Results" @default.
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- W2569793486 doi "https://doi.org/10.1007/978-981-10-3611-8_42" @default.
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