CHAOTIC SIMULATED ANNEALING BY A NEURAL-NETWORK MODEL WITH TRANSIENT CHAOS

We propose a neural network model with transient chaos. or a transient ly chaotic neural network (TCNN) as mt approximation method for combin atorial optimization problems, by introducing transiently chaotic dyna mics into neural networks. Unlike conventional neural networks only wi th point attractors, the proposed neural network has richer and more f lexible dynamics, so that ii can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significa nt property of this model is that the chaotic neurodynamics is tempora rily generated for searching and self-organizing, and eventually vanis hes with autonomous decrease of a bifurcation parameter corresponding to the ''temperature'' in the usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos. to a dynamical structure similar to such conventional models as the Hopfie ld neural network which converges to a stable equilibrium point. Since the optimization process of the transiently chaotic neural network is similar to simulated annealing, not in a stochastic way but in a dete rministically chaotic way, the new method ir regarded as chaotic simul ated annealing (CSA). Fundamental characteristics of the transiently c haotic neurodynamics me numerically investigated with examples of a si ngle neuron model and the Traveling Salesman Problem (TSP). Moreover, a maintenance scheduling problem for generators in a practical power s ystem is also analysed to verify practical efficiency of this new meth od.