High performing stochastic local search algorithms for the QAP and their performance in dependence to the instance structure and size

Abstract

The Quadratic Assignment Problem (QAP) is a prominent NP-hard combinatorial optimization problem that arises in many real world applications. Solving the QAP to optimality is a very challenging task, and due to this, the QAP is mostly tackled by Stochastic Local Search (SLS) algorithms. Different SLS algorithms, however, achieve different levels of performance when tackling instances of different type, structure, or size. This holds true also for the QAP, where to date, no single SLS algorithm can be said to be the “best” approach for solving the QAP. Here we study the relationship between the relative performance of SLS algorithms for solving a class of real-life like Quadratic Assignment Problem instances and instance size. Experimental results show that population-based metaheuristic algorithms perform very well for solving this class of instances, compared to single point search approaches, and that their relative performance is relatively stable.