Parallel Algorithms for Chaotic Exponential Tabu Search Hardware for Quadratic Assignment Problems

The quadratic assignment problem (QAP) is one of the nondeterministic polynomial (NP)-hard combinatorial optimization problems. One of the heuristic algorithms for the QAP is tabu-search. Exponential tabusearch, which is one of improved versions of the tabu search, was proposed using a neural network, and it was further extended to chaotic tabu search with a chaotic neural network. Chaotic dynamics shows efficient solution search ability, and at the same time, it avoids the local minima problem. We have proposed and built a chaotic exponential tabu search hardware system with switched-current chaotic neuron ICs for size-10 QAPs. The measurement results have confirmed the validity and good performance of the system. In this paper, we propose two parallel processing algorithms for the chaotic exponential tabu search hardware to accelerate the processing speed of the system, in order to solve large-scale QAPs.