A HYBRID NEURAL APPROACH TO COMBINATORIAL OPTIMIZATION

Both the Hopfield neural network and Kohonen's principles of self-orga nization have been used to solve difficult optimization problems, with varying degrees of success. In this paper, a hybrid neural network is presented which combines, for the first time, a new self-organizing a pproach to optimization with a Hopfield network. It is demonstrated th at many of the traditional problems associated with each of these appr oaches can be resolved when they are combined into a hybrid model. Aft er presenting the broad class of 0-1 optimization problems for which t his hybrid neural approach is suited, details of the algorithm are pre sented and convergence properties are discussed. This hybrid neural ap proach is applied to solve a practical sequencing problem from the car manufacturing industry. Performance results are compared with classic al as well as other neural techniques, and conclusions are drawn. Copy right (C) 1996 Elsevier Science Ltd