λ-opt neural networks for quadratic assignment problem

We propose new analog neural approaches to quadratic assignment problems. Our methods are 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 methods can achieve a middle-range search over the possible solutions, and this helps the system neglect shallow local minima and escape from local minima. Results have shown that our methods are comparable to the present champion algorithms, and for two benchmark problems, they are able to obtain better solutions than the previous champion algorithms.