The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem

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Author(s): Cha-Hwa Lin (National Sun Yat-sen University, Taiwan)and Jin-Fu Wang (National Sun Yat-sen University, Taiwan)
Copyright: 2010
Pages: 24
Source title: Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies
Source Author(s)/Editor(s): Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan)and Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
DOI: 10.4018/978-1-61520-757-2.ch011

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Mobile agent planning (MAP) is one of the most important techniques in the mobile computing paradigm to complete a given task in the most efficient manner. To tackle this challenging NP-hard problem, Hopfield-Tank neural network is modified to provide a dynamic approach which not only optimizes the cost of mobile agents in a spatio-temporal computing environment, but also satisfies the location-based constraints such as the starting and ending nodes of the routing sequence which must be the home site of the traveling mobile agent. Meanwhile, the energy function is reformulated into a Lyapunov function to guarantee the convergence to a stable state and the existence of valid solutions. Moreover, the objective function is designed to estimate the completion time of a valid solution and to predict the optimal routing path. This method can produce solutions rapidly that are very close to the minimum cost of the location-based and time-constrained distributed MAP problem.

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