Annealed neural network combines the characteristics of both simulation annealing and Hopfield-Tank neural network, which are high quality solutions and fast convergence. Mobile agent planning is an important technique of information retrieval systems to provide the minimum cost of the location-aware services in mobile computing environment. By taking the time constraints of effective resources into account and the mobile agent to explore the cost optimization, we modify annealing neural network to design a new energy function and control the annealing temperature in order to deal with the dynamic temporal feature of computing environments. We not only consider the server performance and network latency when scheduling mobile agents, but also investigate the location-based constraints, such as the home site of routing sequence of the traveling mobile agent must be the start and end node. To guarantee the convergent stable state and existence of the valid solution, the energy function is reformulated into a Lyapunov function which is combined with the annealing temperature to form an activation function. The connection weights between the neurons and the activation function of state variables in the dynamic network are devised in searching for the valid solutions. Simulation of different coefficients assess the proposed model and algorithm. Furthermore, Taguchi method is used to obtain the optimal combination factors of annealing neural network. The results show that this research presents the feature of both simulated annealing and Hopfield neural network by providing fast convergence and highly quality. In addition with a larger number of sites, the experimental results demonstrate the benefits of the annealed neural network. This innovation would be applicable to improve the effectiveness of solving optimization problems.