Exponential and chaotic neurodynamical tabu searches for quadratic assignment problems

The authors propose a chaotic neurodynamic searching method for the Quadratic Assignment Problems (QAPs). First, they construct a neural network, whose behaviour is the same as of the conventional tabu search. Using the dynamics of the tabu search neural network, the authors realise the exponential tabu search, whose tabu effect decreases exponentially with time, and they show the effectiveness of this type of exponential tabu search. Next, they extend this novel tabu search to a chaotic version. The chaotic method includes both the effects of the chaotic dynamical search and the exponential tabu search, and exhibits better performance than the conventional and exponential tabu searches. Last, they propose an automatic parameter tuning method and show that the proposed method exhibits high performance even on large QAPs.