Neural Network Based Algorithm for Multi-Constrained Shortest Path Problem

Authors:
Jiyang Dong;Junying Zhang;Zhong Chen
Affiliations:
Department of Physics, Fujian Engineering Research Center for Solid-State Lighting, Xiamen University, Xiamen 361005, P.R. China and National Key Laboratory for Radar Signal Processing, Xidian Uni ...;National Key Laboratory for Radar Signal Processing, Xidian University, Xi'an 710071, P.R. China;Department of Physics, Fujian Engineering Research Center for Solid-State Lighting, Xiamen University, Xiamen 361005, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Multi-Constrained Shortest Path (MCSP) selection is a fundamental problem in communication networks. Since the MCSP problem is NP-hard, there have been many efforts to develop efficient approximation algorithms and heuristics. In this paper, a new algorithm is proposed based on vectorial Autowave-Competed Neural Network which has the characteristics of parallelism and simplicity. A nonlinear cost function is defined to measure the autowaves (i.e., paths). The M-paths limited scheme, which allows no more than Mautowaves can survive each time in each neuron, is adopted to reduce the computational and space complexity. And the proportional selection scheme is also adopted so that the discarded autowaves can revive with certain probability with respect to their cost functions. Those treatments ensure in theory that the proposed algorithm can find an approximate optimal path subject to multiple constraints with arbitrary accuracy in polynomial-time. Comparing experiment results showed the efficiency of the proposed algorithm.