Solving QAP with Auto-parameterization in Parallel Hybrid Metaheuristics
Abstract
The Quadratic Assignment Problem (QAP) is one of the most challenging combinatorial optimization problems with many reallife applications. Currently, the best solvers are based on hybrid and parallel metaheuristics, which are actually highly complex and parametric methods. Finding the best set of tuning parameters for such methods is a tedious and error-prone task. In this paper, we propose a strategy for auto-parameterization of QAP solvers. We show evidence that autoparameterization can further improve the quality of computed solutions. Our auto-parameterization scheme relieves the user from having to find the right parameters while providing a high quality solution.