IEEE Transactions on Software Engineering
Seven good reasons for mobile agents
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Parameter setting of the Hopfield network applied to TSP
Neural Networks
Agent Chaining: An Approach to Dynamic Mobile Agent Planning
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Cost Effective Mobile Agent Planning for Distributed Information Retrieval
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Cost-Effective Planning of Timed Mobile Agents
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
Mobile agent planning problems
Mobile agent planning problems
d-Agent: an approach to mobile agent planning for distributed information retrieval
IEEE Transactions on Consumer Electronics
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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. Although Hopfield-Tank neural network has been proposed for solving the traveling salesperson problem, little attention has been paid to the time constraints on resource validity for optimizing the cost of the mobile agent. Consequently, we modify Hopfield-Tank neural network and design a new energy function to not only cope with the dynamic temporal features of the computing environment, in particular the server performance and network latency when scheduling mobile agents, but also satisfy the location-based constraints such as the starting and end node of the routing sequence must be the home site of the traveling mobile agent. In addition, the energy function is reformulated into a Lyapunov function to guarantee the convergent stable state and existence of the valid solution. Moreover, the objective function is derived to estimate the completion time of the valid solutions and predict the optimal routing path. Simulations study was conducted to evaluate the proposed model and algorithm for different time variables and various coefficient values of the energy function. 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.