Task planning using immune tabu search genetic algorithm for satellite swarm earth observations

Large satellite swarms are being assigned ever more tasks with increasingly complex observation needs. Effective task planning for satellite swarms has emerged as a focus of research. This paper proposes the immune tabu search genetic algorithm (IMTS-GA), which combines the genetic algorithm, the immune evolutionary algorithm, and tabu search to effectively address this satellite task-planning problem. The IMTS-GA includes a novel method of gene coding involving observation windows that enable repeated observations of targets for a specified observation frequency. A constraint adjustment operator is applied during the iterative process to resolve conflicts, ensuring the solution’s feasibility. An immune operator is introduced to control the evolutionary direction, and a two-phase mutation operator is employed to widen the search range and prevent convergence to a local optimum. The stability and effectiveness of the IMTS-GA were verified through simulations of three scenarios involving various numbers of satellites and targets. The fitness at convergence was consistent for different population sizes, indicating the algorithm’s stability. Moreover, the IMTS-GA achieved better performance at convergence than comparable algorithms did, confirming the effectiveness of the algorithm for satellite swarm task planning.