Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem

Authors:
Yiqiao Cai;Jiahai Wang;Jian Yin;Caiwei Li;Yunong Zhang
Affiliations:
Department of Computer Science, Sun Yat-sen University, Guangzhou, P.R.China 510275;Department of Computer Science, Sun Yat-sen University, Guangzhou, P.R.China 510275;Department of Computer Science, Sun Yat-sen University, Guangzhou, P.R.China 510275;Department of Computer Science, Sun Yat-sen University, Guangzhou, P.R.China 510275;Department of Computer Science, Sun Yat-sen University, Guangzhou, P.R.China 510275
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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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.