Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
This work addresses the NP-hard QAP, which has applications in industrial and logistics environments, by proposing an incremental improvement to existing metaheuristic methods.
The authors tackled the Quadratic Assignment Problem (QAP) by introducing Kernels of Mallows Model under the Hamming distance within Estimation of Distribution Algorithms (EDAs), resulting in superior performance compared to classical and specific EDAs for permutation problems.
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard problem represents, it has captured the attention of the optimization community for decades. As a result, a large number of algorithms have been proposed to tackle this problem. Among these, exact methods are only able to solve instances of size $n<40$. To overcome this limitation, many metaheuristic methods have been applied to the QAP. In this work, we follow this direction by approaching the QAP through Estimation of Distribution Algorithms (EDAs). Particularly, a non-parametric distance-based exponential probabilistic model is used. Based on the analysis of the characteristics of the QAP, and previous work in the area, we introduce Kernels of Mallows Model under the Hamming distance to the context of EDAs. Conducted experiments point out that the performance of the proposed algorithm in the QAP is superior to (i) the classical EDAs adapted to deal with the QAP, and also (ii) to the specific EDAs proposed in the literature to deal with permutation problems.