A coupled gradient network approach for static and temporal mixed-integer optimization

The purpose of this work is to utilize the ideas of artificial neural networks to propose new solution methods for a class of constrained mixed-integer optimization problems, These new solution methods are more suitable to parallel implementation than the usual sequential methods of mathematical programming, Another attractive feature of the proposed approach is that some mechanisms of global search may be easily incorporated into the computation, producing results which are more globally optimal, To formulate the method of solution proposed in this work, a penalty function approach is used to define a coupled gradient-type network with an appropriate architecture. energy function, and dynamics such that high quality solutions may be obtained upon convergence of the dynamics, Finally, it is shown how the coupled gradient net mag be extended to handle temporal mixed-integer optimization problems, and simulations are presented which demonstrate the effectiveness of the approach.