Solving the mobile agent planning problem with a Hopfield-Tank neural network

Mobile agent planning (MAP) is increasingly viewed as an important technique of information retrieval systems to provide location aware services of minimum cost in mobile computing environment. However, little attention has been paid to the time constraints on resource validity for optimizing the cost of the mobile agent in the research literature. We hypothesized that Hopfield-Tank neural network can be used to solve the MAP problem. Consequently, we propose a modified Hopfield-Tank neural network to cope with the dynamic temporal features of the computing environment, in particular the server performance and network latency when scheduling mobile agents. In addition, Liapunov energy function is reformulated to satisfy the location-based constraints especially the starting and end node of the routing sequence must be the same as the home site of the traveling mobile agent. A prototype system using the presented architecture and algorithm is developed and implemented by simulation method. The experimental results quantitatively demonstrate the computational power and speed of the proposed model by producing solutions that are very close to the minimum costs of the location-based and time-constrained distributed MAP problem rapidly. The spatio-temporal technique proposed in this work is an innovative approach in providing knowledge applicable to improving the effectiveness of solving optimization problems.