47 / 2025-03-29 13:56:00

A Hybrid Genetic Algorithm for Multi-Satellite Mission Planning Problem

mission planning,earth observation satellite,Genetic Algorithm (GA),simulated annealing algorithm

The traditional genetic algorithm tends to fall into local optimality when solving large scale satellite mission planning problem, which makes it difficult to attain better planning results. The improvement of the algorithm's optimization search capability for higher gains in satellite observation missions is of great research interest. In this paper, a new hybrid genetic algorithm is proposed for the comprehensive optimization of the multi-objective satellite mission planning problem to maximize the satellite observation gain while minimizing the energy consumption of the satellites. An integer coding method is designed in the algorithm for the characteristics of satellite mission planning. The algorithm adopts a hybrid initialization method to improve the quality of the initial population while maintaining the diversity of the initial population. The adaptive genetic variation method and simulated annealing elite selection strategy are used in the evolutionary process of the algorithm. The combination of the global optimality-seeking ability of the genetic algorithm and the local optimality-seeking ability of the simulated annealing algorithm improves the optimality-seeking ability; the possibility of the algorithm falling into a local optimum is reduced by the adaptive mutation strategy. Finally, the effectiveness and superiority of the hybrid genetic algorithm is verified through experiments.