Locally interconnected layered neural network for path optimization

Highly interconnected networks of relatively simple processing elements are shown to be very effective in solving difficult optimization problems. Problems that fall into the broad category of finding a least-cost path between two points, given a distributed and sometimes complex cost map, are studied. A neurallike architecture and associated computational rules are proposed for the solution of this class of optimal path-finding problems in two- and higher-dimensional spaces. The proposed algorithm is local in nature and is very well suited for highly parallel, fine-grained, and distributed architectures. Also described is a collective multilayer neurallike architecture, characterized by speed of convergence, scalability, and guaranteed convergence to optimal solutions