Satellite formation reconfiguration using co-evolutionary particle swarm optimization and Pareto optimal solution

This paper proposes an optimal trajectory planning method for satellite formation reconfiguration using co-evolutionary particle swarm optimization (CPSO) and Pareto optimal solution. First, the Legendre pseudospectral method (LPM) is employed to transform the reconfiguration problem into a parameter optimization nonlinear programming (NLP) problem. Next, according to the features of satellite formation and the constraints of collision avoidance, a CPSO algorithm is used to solve the reconfiguration problem separately in a centralized way to avoid the computational complexity of calculating the gradient information with traditional optimization methods. Then, a depth-breadth first search (D-BFS) algorithm is used to search all the Pareto optimal solutions needed by the CPSO, with which the entire redundant search could be avoided. Simulations show that the method could solve the reconfiguration problem in real time, and guarantee collision avoidance during the entire reconfiguration process even when the number of collocation points or number of satellites increases.