Spacecraft cluster orbit planning method based on adaptive population mutated pigeon group optimization

Abstract

The orbit planning problem of spacecraft clusters under complex conditions is a hotspot and a difficult goal in the current aerospace field. This paper studies the orbital optimal programming problem of distributed cluster spacecraft in the process of formation transformation and proposes the population evolution algorithm adaptive population variation pigeon-inspired optimization (APVPIO). Based on the core evolutionary algorithm and evolutionary stagnation, there is a high tendency to fall into the local optimal solution in the classical PIO algorithm. The fitness function of the classical PIO algorithm is studied and improved with the orbit planning problem. Finally, the simulation based on the adaptive population variation algorithm is performed. The results reveal that the APVPIO algorithm has better planning results, deeper population evolution depth, and a faster convergence speed in comparison to the classical PIO algorithm and particle swarm algorithm (PSO) algorithm. Hence, it has the potential to meet the complexity requirement of spacecraft clusters and orbital planning problems.