A novel approach for combinatorial optimization problems using chaotic neurodynamics

We propose a novel approach for combinatorial optimization using chaotic dynamics. First, we realize the conventional tabu search on a neural network, and we modify it to a chaotic version. Then, our novel method includes both effects of chaotic dynamics and tabu search, which would be effective for combinatorial optimization. We also propose a parameter turning method for effective and fast search of our chaotic neural network. We show that our novel method exhibits better than one of the strongest tabu search, for various quadratic assignment problems, without manual parameter setting.