Xiaolu Liu

Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolution

A novel self-adaptive variable-size multi-objective differential evolution algorithm is presented to find the best reconfiguration of existing on-orbit satellites for some particular targets on the ground when an emergent requirement arises in a short period. The main contribution of this study is that three coverage metrics are designed to assess the performance of the reconfiguration. Proposed algorithm utilizes the idea of fixed-length chromosome encoding scheme combined with expression vector and the modified initialization, mutation, crossover and selection operators to search for optimal reconfiguration structure. Multi-subpopulation diversity initialization is adopted first, then the mutation based on estimation of distribution algorithm and adaptive crossover operators are defined to manipulate variable-length chromosomes, and finally a new selection mechanism is employed to generate well-distributed individuals for the next generation. The proposed algorithm is applied to three characteristically different case studies, with the objective to improve the performance with respect to specified targets by minimizing fuel consumption and maneuver time. The results show that the algorithm can effectively find the approximate Pareto solutions under different topological structures. A comparative analysis demonstrates that the proposed algorithm outperforms two other related multi-objective evolutionary optimization algorithms in terms of quality, convergence and diversity metrics.

An adaptive large neighborhood search metaheuristic for agile satellite scheduling with time-dependent transition time

Abstract Agile satellites belong to the new generation of satellites with three degrees of freedom for acquiring images on the Earth. As a result, they have longer visible time windows for the requested targets. An image shot can be conducted at any time in the window if and only if the time left is sufficient for the fulfillment of the imaging process. For an agile satellite, a different observation time means a different image angle, thus defining a different transition time from its neighboring tasks. Therefore, the setup time for each imaging process depends on the selection of its observation start time, making the problem a time-dependent scheduling problem. To solve it, we develop a metaheuristic, called adaptive large neighborhood search (ALNS), thus creating a conflict-free observational timeline. ALNS is a local search framework in which a number of simple operators compete to modify the current solution. In our ALNS implementation, we define six removal operators and three insertion operators. At each iteration, a pair of operators is selected to destroy the current solution and generate a new solution with a large collection of variables modified. Time slacks are introduced to confine the propagation of the time-dependent constraint of transition time. Computational experiments show that the ALNS metaheuristic performs effectively, fulfilling more tasks with a good robustness.

Optimal Satellite Orbit Design for Prioritized Multiple Targets with Threshold Observation Time Using Self-Adaptive Differential Evolution

AbstractThis study considers a satellite orbit design when a given set of target sites needs to be visited within an assigned period. Optimization of the orbit design takes into account target properties such as priority and minimum duration of observation time. Equations describing satellite orbit are used to compute the satellite’s field of view and ground track. Scheduling of visits to the targets is carried out by maximizing the observation time over priority-weighted targets and minimizing the distance between the ground track and the target position while satisfying the constraints with respect to the minimum duration of observation time and revisit requirement. A self-adaptive differential evolution (SA-DE) algorithm with the ability to adjust its search parameters is used to find the best solution. The proposed design methodology computes the optimal values of design variables and also the start and end observation time points of each target. Regional and global case studies demonstrate that the S...

Design of Experiment Method for Microsatellite System Simulation and Optimization

A system optimization framework based on design of experiment (DOE) and simulation is proposed. Simulation is effective in the optimization of many complex systems, however, it is time consuming and tough. DOE can reduce the number of test cases drastically while ensuring input combination is of wise coverage of design space. A framework incorporating simulation and DOE is proposed, which provides a methodology to deal with optimization problem. It contains two key technologies: Latin hypercube design and surrogate model optimization, which are all detailed in paper. A problem of micro satellite system optimization is proposed to illustrate the correctness and effectiveness of the framework. The final results are listed. Also effect of each design variable on the system performance is analyzed.

An improved adaptive large neighborhood search algorithm for multiple agile satellites scheduling

Abstract The multiple agile satellites scheduling problem is a time-dependent scheduling problem which is considerably more difficult than the single agile satellite scheduling problem, due to its much larger solution space. We extend the adaptive large neighborhood search (ALNS) developed for the single satellite scheduling problem to the multiple satellite case. An adaptive task assignment mechanism is introduced into the ALNS framework by defining five assignment operators. In the adaptive task assignment based ALNS (A-ALNS), the removal operators remove tasks from the current solution, the insertion operators insert tasks in the destroyed solution, and if the solution has not improved for a number of iterations, the assignment operators will reassign tasks to different satellites. These operators are selected adaptively to guide the algorithm to search the solution space efficiently. The effect of the parameters on the algorithm performance is studied in the simulation experiments, and the operators are also compared. Extensive computational results show that the proposed adaptive task assignment mechanism is more efficient than competing state-of-the-art multi-satellite processing methods. The A-ALNS metaheuristic performs effectively, handling the complexity brought by the large number of satellites and fulfilling more tasks with a good robustness.