A hybrid neural approach to combinatorial optimization

Both the Hopfield neural network and Kohonen’s principles of self-organization 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 approach to optimization with a Hopfield network. It is demonstrated that many of the traditional problems associated with each of these approaches can be resolved when they are combined into a hybrid model. After presenting the broad class of 0-1 optimization problems for which this hybrid neural approach is suited, details of the algorithm are presented and convergence properties are discussed. This hybrid neural approach is applied to solve a practical sequencing problem from the car manufacturing industry. Performance results are compared with classical as well as other neural techniques, and conclusions are drawn.