Solving shortest path problem using particle swarm optimization
Applied Soft Computing
Motion planning in order to optimize the length and clearance applying a Hopfield neural network
Expert Systems with Applications: An International Journal
Find multi-objective paths in stochastic networks via chaotic immune PSO
Expert Systems with Applications: An International Journal
WSEAS TRANSACTIONS on COMMUNICATIONS
A novel multi-objective evolutionary algorithm for shortest path routing problem
International Journal of Communication Networks and Distributed Systems
A biologically inspired solution for fuzzy shortest path problems
Applied Soft Computing
A Neural-network Algorithm for All k Shortest Paths Problem
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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This paper presents a new neural network to solve the shortest path problem for inter-network routing. The proposed solution extends the traditional single-layer recurrent Hopfield architecture introducing a two-layer architecture that automatically guarantees an entire set of constraints held by any valid solution to the shortest path problem. This new method addresses some of the limitations of previous solutions, in particular the lack of reliability in what concerns successful and valid convergence. Experimental results show that an improvement in successful convergence can be achieved in certain classes of graphs. Additionally, computation performance is also improved at the expense of slightly worse results