Mutual information analyses of chaotic neurodynamics driven by neuron selection methods in synchronous exponential chaotic tabu search for quadratic assignment problems

The exponentially decaying tabu search, which exhibits high performance in solving quadratic assignment problems (QAPs), has been implemented on a neural network with chaotic neurodynamics. To exploit the inherent parallel processing capability of analog hardware systems, a synchronous updating scheme, in which all neurons in the network are updated simultaneously, has also been proposed. However, several neurons may fire simultaneously with the synchronous updating. As a result, we cannot determine only one candidate for the 2-opt exchange from among the many fired neurons. To solve this problem, several neuron selection methods, which select a specific neuron from among the fired neurons, have been devised. These neuron selection methods improved the performance of the synchronous updating scheme; however, the dynamics of the chaotic neural network driven by these heuristic algorithms cannot be intuitively understood. In this paper, we analyze the dynamics of a chaotic neural network driven by the neuron selection methods by considering the spatial and temporal mutual information.