TOGA-Based Fuzzy Grey Cognitive Map for Spacecraft Debris Avoidance

Abstract

The optimization of fuzzy grey cognitive map (FGCM) can enhance the decision quality of the system in managing uncertainties and incomplete information. Addressing this issue requires a method that effectively balances the competing demands of the speed and precision in the optimization process. Therefore, a tradeoff genetic algorithm (TOGA) is proposed to refine the FGCM optimization process in this article. First, a modified fitness function with a penalty term is designed to improve the convergence rate, which can drive the FGCM population to obtain the optimal solution during the evolutionary process. Second, an adaptive genetic mechanism based on horizontal comparison and longitudinal assessment, is designed to strike a balance between accelerating convergence and avoiding the dilemma of falling into local optima. Finally, in the simulation section, the effectiveness of the proposed method is validated by optimizing FGCM using synthetic datasets and applying it toa spacecraft debris threat avoidance scenario.