Optimization of imaging reconnaissance constellation based on relative entropy of fuzzy sets

The optimization of imaging reconnaissance constellation is of great significance for reconnaissance timeliness. At present, evolutionary algorithms based on Pareto are often used in the optimization of reconnaissance constellation. In order to solve the problems of insufficient selection pressure and poor diversity of such algorithms in the optimization of reconnaissance constellation whose objective function dimension is greater than three, the improved particle swarm optimization algorithm based on relative entropy of fuzzy sets(IFREM-PSO) is proposed. The algorithm improves the adaptive inertial weight strategy and enhances the convergence speed and accuracy. The introduction of variation strategy is conducive to jumping out of the local optimal solution. Improve external archive maintenance policy to enhance diversity. Based on the design and optimization of regional target-oriented visible light reconnaissance constellation, MOPSO, FREM-PSO and the IFREM-PSO are used to optimize the reconnaissance constellation. The experimental results show that the algorithm based on fuzzy relative entropy has a better performance in this problem, and compared with FREM-PSO algorithm, IFREM-PSO algorithm has a significant improvement in convergence speed, and a better performance in convergence effect and diversity.