Abstract
In this paper, we propose a neural network algorithm—multi-start stochastic competitive Hopfield neural network (MS-SCHNN) for the p-median problem. The proposed algorithm combines two mechanisms to improve neural network’s performance. First, it introduces stochastic dynamics into the competitive Hopfield neural network (CHNN) to help the network escape from local minima. Second, it adopts multi-start strategy to further improve the performance of SCHNN. Experimental results on a series of benchmark problems show that MS-SCHNN outperforms previous neural network algorithms for the p-median problem.
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© 2009 Springer-Verlag Berlin Heidelberg
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Cai, Y., Wang, J., Yin, J., Li, C., Zhang, Y. (2009). Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_10
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DOI: https://doi.org/10.1007/978-3-642-01507-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
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