Research And Application Of OMEA In The Optimal Design Of Constellation

In recent ten years, Evolution Algorithm has developed into an ideal approach to solve multi-objective optimization problems, especially the large-scale, complex ones. Thus multi-objective optimization problem has become the research hot point in the field of Evolutionary Algorithm.Further more, multi-objective evolutionary algorithm has been applied to the aerospace application. The constellation composed of multi-satellites can provide communication services for broad geographical scope, which is a useful supplement to terrestrial communication network. It is of great value to the military activities, the government, and various of industry segments. It is a key issue to design efficient and feasible constellation layout for an specific application. However, constellation itself is very complex. It is a multi-dimensional, dynamic, multi-objective problem. For the coverage performance of constellation is closely related to the orbital elements, traditional optimization methods can't cater to the competing performance index. So in the past ten years, the evolutionary algorithm has been introduced to the constellation design. In particular, the multi-objective evolutionary algorithm has achieved better result in regional constellation design in recent years. However, there are some common disadvantages of traditional multi-objective evolutionary algorithm such as low efficiency, weak convergence, and not make use of distribution rule of pareto set. How to apply the improved or new multi-objective evolutionary algorithm to constellation design to improve the performance is another key issue of the paper. All the studies will also develop multi-objective evolutionary algorithm as well as its application in constellation design.The paper first presents background of multi-objective evolutionary algorithm. Then, for the deficiency of evolutionary algorithm in solving multi-objective optimization problem, OMEA is proposed to enhance the robustness and local search capability of the algorithm. Besides good convergence, the new algorithm based on the traditional model evolutionary algorithm has some new features. 1,generate initial population with orthogonal design so that the individuals make a more representative distribution of the feasible solutions.2,introduce the idea of probabilistic model, establish model based on pareto set distribution rule to enhance local search ability and then improve search performance of the algorithm.3, replace L-PCA cluster analysis algorithm in MMEA with k-means clustering analysis algorithm to improve efficiency of the algorithm. To test the performance of the improved algorithm, the paper does numerical experiments with some of the current representative functions, and does comparison to other multi-objective evolutionary algorithms. The results show that the new algorithm OMEA achieves much better performance in both diversity and convergence of the solutions, and avoids premature convergence to a local Pareto solution set. The performance is clearly superior to the traditional multi-objective evolutionary algorithms. After validating the improved algorithm and introducing the mathematical and physical models as well as related astrodynamics knowledge, Chapter 4 presents process and scheme of the regional constellation design based on evolutionary algorithm, and applies OMEA proposed forward to two instances of LEO/MEO regional constellation coverage to test the performance of OMEA for solving constellation optimization problem, and compares the result with NSGA-Ⅱand traditional mathematical derivation. The results show that accuracy and convergence of solutions of OMEA is superior to the other two methods. Each target coverage rate obtained by OMEA is above 99% which can better meet regional continuous coverage requirements. It proves OMEA can play a good role in regional communications constellation design. Further more, the Pareto set contains more possible solutions, thus OMEA has greater flexibility. So policy maker can select suitable constellation according to preferred target or experience. In addition, the paper gives a brief introduction to the visualization platform for constellation design. The platform provides accurate assessment to three-dimensional position of the constellation, footprint and coverage characteristics of ground sites. It is of great directive meaning to the practical optimal design of constellation.The main innovation is as follows: 1)propose a new model multi-objective evolutionary algorithm. 2)construct process of regional constellation optimal design based on evolutionary algorithm, and apply the new algorithm to constellation optimal design field. 3)develop a visualization platform for constellation design, which is able to do visualization simulation according to the optimal data and do evaluation of real-time performance to constellation scheme.The main sections are as follows:The first chapter introduces the background, the purpose and significance of the research, summarizes the traditional methods solving multi-objective optimization problem and their advantages and disadvantages, and gives a simple overview of development history of multi-objective evolutionary algorithm and constellation optimal design problem.The second chapter introduces the basic concepts and related definition of the multi-objective optimization problem at first. Then, introduces the current main multi-objective algorithm and the problems to be solved, and considering the evolutionary algorithm not make use of Pareto distribution rules, proposes a new model multi-objective evolutionary algorithm by importing idea of probabilistic model, orthogonal initialization, K-means and Principal component analysis(PCA).At last, tests the performance of OMEA with current popular benchmarks for multi-objective algorithm test, and compares OMEA with other classical multi-objective algorithms.The third chapter presents the constellation model, basic theory of aerospace dynamics and the LEO/MEO perturbation analysis. Finally, it introduces numerical simulation evaluation method of satellite coverage performance.The fourth chapter briefly describes process of constellation design with evolutionary algorithm, then designs two practical LEO/MEO regional constellation design and evaluates the performance. In the end, gives a brief presentation to the constellation simulation software.The fifth chapter summarizes the main work of the paper and describes the future work.