Study on optimization of agent initial positions in land combat simulation

Abstract

The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that of using the traditional SVM with the same precision. We also summarize and present some experiences and trends on the optimization problem in land combat simulation.