A new metaheuristic, the teaching-learning-based optimisation TLBO metaheuristic, based on the relationship between teachers and learners has recently been proposed by Rao, Savsani and Vakharia 2011 for solving continuous nonlinear optimisation problems. It is of particular interest because it is a population-based metaheuristic that can be easily adapted to solve combinatorial optimisation problems and requires no parameter fine-tuning other than determining the size of the population and convergence criteria. In this paper, we enhance the performance of the TLBO method by introducing 'teacher training' before the teaching phase of TLBO. That is, before the teaching phase of TLBO, we perform a local neighbourhood search on the best solution the teacher in the current population. The effectiveness of teacher training TT in terms of both solution quality and convergence rate will be demonstrated by using this approach TT-TLBO to solve a large 393 number of problem instances from the literature for the important NP-Hard multiple-choice multidimensional knapsack problem MMKP. Furthermore, we will demonstrate that TLBO outperforms the best published solution approaches for the MMKP.
['Kenneth Zyma', 'Yun Lu', 'Francis J. Vasko']
Teacher training enhances the teaching-learning-based optimisation metaheuristic when used to solve multiple-choice multidimensional knapsack problems