A hybrid neural approach to combinatorial optimization

Both the Hopfield neural network and Kohonenʹs 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.