Heuristic optimization of resource allocation in space based early-warning system

Space based early-warning is the monitoring and predicting process with the property of the discrete time sequence. Many decision-making factors, optimization objective functions and constraint conditions need be considered when scheduling the sensor tasks, as a result, the intelligent optimization algorithm is typically adopted to solve this nonlinear optimization problem. However these optimization methods are convergence to the Pareto solution in probability within the specified time. Three respects of work are done in this paper, firstly, the Decision Tree is applied to mine the heuristic knowledge; secondly, the Smoothed Naive Bayes is presented in order to provide the multi-class Decision Tree; finally, the heuristic knowledge is fused into the intelligent optimization algorithm, where local search operator is added. The experiment shows that Immune Colonal Selection increases 10.1% in terms of convergence probability, and Gene Algorithm increases 9.8%.