Qiu Dishan

A dynamic scheduling method of Earth-observing satellites by employing rolling horizon strategy.

Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments.

Cooperative Scheduling of Imaging Observation Tasks for High-Altitude Airships Based on Propagation Algorithm

The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA). Then, a novel swarm intelligence algorithm named propagation algorithm (PA) is combined with the key node search algorithm (KNSA) to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible.

An improved genetic algorithm for task scheduling of electro-magnetic detection satellite with uncertain detecting duration

Electro-magnetic Detection Satellite(EDS) is an important branch of Earth Observation Satellites (EOSs). It has been widely applied in industry and military areas. The detecting duration of EDS is different from imagery satellites, for it is an imprecise parameter because of the uncertain electromagnetic environment within space surrounding targets. This factor makes the task scheduling for EDS becoming a complex combinatorial optimization problem. With consideration of this special property, we used fuzzy set and possibility theory to model imprecise parameters, other physical constraints, like on-board energy and transition time between different working patterns and sensors rebooting, were also taken into account in the model. We presented an improved genetic algorithm to solve this problem by introducing new method of elitist and parents selection. The model and the algorithm have been tested by five experiments derived from STKs satellite database.

Imaging Tasks Scheduling for High-Altitude Airship in Emergency Condition Based on Energy-Aware Strategy

Aiming to the imaging tasks scheduling problem on high-altitude airship in emergency condition, the programming models are constructed by analyzing the main constraints, which take the maximum task benefit and the minimum energy consumption as two optimization objectives. Firstly, the hierarchy architecture is adopted to convert this scheduling problem into three subproblems, that is, the task ranking, value task detecting, and energy conservation optimization. Then, the algorithms are designed for the sub-problems, and the solving results are corresponding to feasible solution, efficient solution, and optimization solution of original problem, respectively. This paper makes detailed introduction to the energy-aware optimization strategy, which can rationally adjust airships cruising speed based on the distribution of tasks deadline, so as to decrease the total energy consumption caused by cruising activities. Finally, the application results and comparison analysis show that the proposed strategy and algorithm are effective and feasible.

Solving Bi-level Programming Problem Based on Electromagnetism-Like Algorithm

Bilevel programming problem is NP-hard and not easy to be solved. In the paper, we studied and designed an optimization algorithm for bilevel programming problems based on Electromagnetism-like mechanism. The experiment results showed that the proposed algorithm is feasible and more efficient that existing algorithms based on evolutionary mechanism.

A Constrained non-dominated sorting genetic algorithm of sequential tasks planning for Multi-EOS

Multi-EOS (Earth Observing Satellites) tasks planning problem is a typical over-subscribed resource allocation problem with multi-objective, especially to make schedule for both LEO (Low Earth Orbit) and HEO (High Earth Orbit) satellites. The optimization work of scheduling is assigning appropriate time window to every request (including data transmission process between satellites and ground facilities) under physical and task precedence constraints. In this paper, we construct a mixed integer programming model for this problem. Then we proposed a multi-objective constrained non-dominated sorting genetic algorithm based on the strategy of elitist selection, in this algorithm, a fast non-dominated sorting approach with computational complexity of O(MN2) is used, to be able to keep a better spread of solutions and better convergence near the true Pareto-optimal front, crowd-distance comparison operator is used and get a better result compare to Strength Pareto Evolutionary Algorithm, constraints control are also used to guarantee the feasibility of solutions. The approach is tested upon three applications derived from satellites data base of AGIs Satellite toolkit.