Title: Teacher training enhances the teaching-learning-based optimisation metaheuristic when used to solve multiple-choice multidimensional knapsack problems
Authors: Kenneth Zyma; Yun Lu; Francis J. Vasko
Addresses: Computer Science Department, Kutztown University, P.O. Box 730 Kutztown, PA 19530 USA ' Department of Mathematics, Kutztown University, P.O. Box 730 Kutztown, PA 19530 USA ' Department of Mathematics, Kutztown University, P.O. Box 730 Kutztown, PA 19530 USA
Abstract: 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.
Keywords: combinatorial optimisation; metaheuristics; multidimensional knapsack problems; teacher training; multiple-choice knapsack problems; teaching-learning-based optimisation; nonlinear optimisation; local search.
International Journal of Metaheuristics, 2015 Vol.4 No.3/4, pp.268 - 293
Available online: 29 Jan 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article