Mutation Hopfield neural network and its applications

Authors:
Laihong Hu;Fuchun Sun;Hualong Xu;Huaping Liu;Xuejun Zhang
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China and Xi'an Research Institute of High-tech, Xi'an, Shannxi 710025, PR China;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China;Xi'an Research Institute of High-tech, Xi'an, Shannxi 710025, PR China;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China;School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, PR China
Venue:
Information Sciences: an International Journal
Year:
2011

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Abstract

In this paper, a new operator is proposed to optimize the traditional Hopfield neural network (HNN). The key idea is to incorporate the global search capability of the Estimation of Distribution Algorithms (EDAs) into the HNN, which typically has a powerful local search capability and fast operation. On account of this property of the EDA, our proposed algorithm also exhibits a powerful global search capability. In addition, the possible infeasible solutions generated during the re-sampling period of the EDA are eliminated by the HNN. Therefore, the merits of both these methods are combined in a unified framework. The proposed model is tested on a numerical example, the max-cut problem. The new and optimized model yielded a better performance than certain traditional intelligent optimization methods, such as HNN, genetic algorithm (GA). The proposed mutation Hopfield neural network (MHNN) is also used to solve a practical problem, aircraft landing scheduling (ALS). Compared with first-come-first-served sequence, MHNN sequence reduces both total landing time and total delay.