Hybrid teaching–learning-based optimization algorithms for the Quadratic Assignment Problem. (July 2015) - Dokeroglu, Tansel
Highlights: Teaching–learning-based method is applied to the QAP. The proposed algorithms make use of a Robust Tabu Search (RTS) engine. There is no need for any selection mechanism for the algorithms. Parallel versions of the algorithms are developed for harder problems. Proposed algorithms perform better than most of the state-of-the-art algorithms. Abstract: Teaching–Learning-Based Optimization (TLBO) is a novel swarm intelligence metaheuristic that is reported as an efficient solution method for many optimization problems. It consists of two phases where all individuals are trained by a teacher in the first phase and interact with classmates to improve their knowledge level in the second phase. In this study, we propose a set of TLBO-based hybrid algorithms to solve the challenging combinatorial optimization problem, Quadratic Assignment. Individuals are trained with recombination operators and later a Robust Tabu Search engine processes them. The performances of sequential and parallel TLBO-based hybrid algorithms are compared with those of state-of-the-art metaheuristics in terms of the best solution and computational effort. It is shown experimentally that the performance of the proposed algorithms are competitive with the best reported algorithms for the solution of the Quadratic Assignment Problem with which many real life problems can be modeled.