Analog λ-opt approach to quadratic assignment problem

In this paper, we propose a novel analog neural approach to combinatorial optimization problems, in particular, the quadratic assignment problem (QAP). Our proposed method is based on an analog version of the λ-opt heuristics, which simultaneously changes assignments for λ elements in a permutation. Since we can take a relatively large λ value, our method can achieve a middle-range search over the possible solutions, and this helps the system neglect shallow local minima and escape from local minima. In experiments, we have applied our method to relatively large-scale (N = 80 to 150) QAPs. Results have shown that our new method is comparable to the present champion algorithms, and for two benchmark problems, it is able to obtain better solutions than the previous champion algorithms.