PDF(1510 KB)
基于多目标进化算法的侦察星座优化方法研究*
刘亚丽, 熊伟
PDF(1510 KB)
中国指挥与控制学会会刊
军事装备类重点期刊
基于多目标进化算法的侦察星座优化方法研究*
Research on optimization method of reconnaissance constellation based on multi-objective evolutionary algorithm
为进一步解决侦察星座优化问题,加强天基侦察体系的建设,对当前侦察星座优化方法进行研究与综述。侦察星座的设计与优化具有多参数、多目标、非线性、不连续等特点,是一种典型的多目标优化问题。多目标进化算法是一类以种群为基础的、根据预定启发式规则在决策空间内进行概率搜索的生物智能算法,无需问题满足连续性、可微性等条件,能够在有限的搜索次数内得到一组靠近Pareto前沿的解集,能够有效解决多目标优化问题,可用于侦察星座的优化。论述了侦察星座优化模型的构建、多目标进化算法的分类及优缺点,并对多目标进化算法在侦察星座优化中的应用进行了分析,给出了基于多目标进化算法的侦察星座优化的发展方向。
In order to further solve the optimization problem of reconnaissance constellation and strengthen the construction of space-based reconnaissance system, the current optimization methods of reconnaissance constellation are studied and summarized. The design and optimization of reconnaissance constellation has the characteristics of multi-parameter, multi-objective, nonlinear and discontinuous, which is a typical multi-objective optimization problem. Multi-objective evolutionary algorithm is a kind of biological intelligence algorithm based on population, which carries out probabilistic search in decision space according to predetermined heuristic rules. It does not need continuity, differentiability and other conditions of the problem, and can get a group of solution sets close to Pareto frontier within a limited number of search times. It can effectively solve the multi-objective optimization problem and has been widely used in the multi-objective optimization of reconnaissance constellation. This paper discusses the construction of the reconnaissance constellation optimization model and the classification, advantages and disadvantages of the multi-objective evolutionary algorithm, discusses the application of the multi-objective evolutionary algorithm in the reconnaissance constellation optimization, and points out the development direction of the reconnaissance constellation optimization based on the multi-objective evolutionary algorithm.
星座优化； 天基侦察； 进化算法； 多目标优化； 高维多目标优化 {{custom_keyword}}；
constellation optimization; space-based reconnaissance; evolutionary algorithm; multi-objective optimization; many-objective optimization {{custom_keyword}};
表1 经典的星座构型及其优缺点Tab.1 Classical constellation configuration and its advantages and disadvantages |
| 星座构型 | 优点 | 缺点 |
|---|---|---|
| Walker星座 | 空间分布均匀,覆盖均匀性好,适用于全球连续覆盖的中/低轨全球卫星系统 | 覆盖性能不及共地面轨迹星座 |
| 共地面轨迹星座 | 所有卫星的地面轨迹相同,对指定区域的覆盖性能优异,便于与地面站进行信息交互 | 测控困难,发射成本大 |
| Flower星座 | 经过合理设计,可以拥有共地面轨迹星座的一切优点,还能够克服多轨道面的共地面轨迹星座测控困难,发射成本大的缺陷 | 作用时段有限,覆盖均匀性不佳 |
| 太阳同步轨道星座 | 可以提供独特的周期重复观察特性 | 覆盖性能不佳 |
表2 经典的进化算法及其特点Tab.2 The characteristics of classical evolutionary algorithms |
| 算法 | 收敛性 | 多样性 | 全局性 |
|---|---|---|---|
| 遗传算法 | 较好,但收敛速度较慢 | 收敛性与多样性需平衡 | 较强,但局部搜索能力较弱 |
| 粒子群算法 | 收敛速度快,但收敛性不强 | 较差 | 较差,容易陷入局部最优 |
| 蚁群算法 | 较好,但收敛速度慢 | 较差 | 较强,但受初始结果影响大 |
| 人工免疫算法 | 较好,收敛速度快 | 较好 | 强,但搜索效率低 |
| 模拟退火算法 | 较差,收敛速度慢 | 中 | 强,局部搜索能力较强 |
| [1] |
郑鹏飞, 陈宏宇, 郭崇滨. 低轨巨型链形星座解析设计及效能分析[J]. 西北工业大学学报, 2022, 40(1): 148-157.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [2] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [3] |
韦娟, 张润. 对地侦察卫星星座优化设计及仿真分析[J]. 西安电子科技大学学报, 2013, 40(2): 138-141, 147.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [4] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [5] |
PALMERINI.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [6] |
戴翠琴, 李剑, 唐煌. 卫星时变网络中基于连接计划的最短路径优化算法[J]. 重庆邮电大学学报(自然科学版), 2017, 29(1): 29-35.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [7] |
唐力群. 非静止轨道卫星通信系统星座及通信链路设计[D]. 哈尔滨: 哈尔滨工业大学, 2015.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [8] |
李基, 邵琼玲. 基于STK/Matlab的Walker星座设计与优化[J]. 兵工自动化, 2017, 36(12): 67-70.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [9] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [10] |
刘思航. 基于遗传算法的全球卫星通信系统星座设计[D]. 北京: 北京邮电大学, 2018.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [11] |
马原野. 近地全球重访星座轨道快速优化设计研究[D]. 北京: 中国科学院大学(中国科学院国家空间科学中心), 2019.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [12] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [13] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [14] |
We describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [15] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [16] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [17] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [18] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [19] |
肖婧, 王科俊, 毕晓君. 基于改进K支配排序的高维多目标进化算法[J]. 控制与决策, 2014, 29(12): 2 165-2 170.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [20] |
毕晓君, 张永建, 陈春雨. 基于模糊支配的高维多目标进化算法MFEA[J]. 电子学报, 2014, 42(8): 1 653-1 659.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [21] |
余伟伟, 谢承旺, 闭应洲, 等. 一种基于自适应模糊支配的高维多目标粒子群算法[J]. 自动化学报, 2018, 44(12): 2 278-2 289.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [22] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [23] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [24] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [25] |
谢承旺, 余伟伟, 闭应洲, 等. 一种基于分解和协同的高维多目标进化算法[J]. 软件学报, 2020, 31(2): 356-373.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [26] |
顾清华, 莫明慧, 卢才武, 等. 求解约束高维多目标问题的分解约束支配NSGA-Ⅱ优化算法[J]. 控制与决策, 2020, 35(10): 2 466-2 474.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [27] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [28] |
乔钢柱, 王瑞, 孙超利. 基于分解的高维多目标改进进化算法[J]. 计算机应用, 2021, 41(11): 3097-3103.
针对基于参考向量的高维多目标进化算法中随机选择父代个体会降低算法的收敛速度，以及部分参考向量分配个体的缺失会减弱种群多样性的问题，提出了一种基于分解的高维多目标改进优化算法（IMaOEA/D）。首先，在分解策略框架下，当一个参考向量至少分配了2个个体时，对该参考向量分配的个体根据其到理想点的距离选择父代个体来繁殖子代，从而提高搜索速度。然后，针对未能分配到至少2个个体的参考向量，则从所有个体中选择沿该参考向量和理想点距离最小的点，使得该参考向量至少有2个个体与其相关。同时，确保环境选择后每个参考向量有一个个体与其相关，从而保证种群的多样性。在10个和15个目标的MaF测试问题集上将所提算法与其他4个基于分解的高维多目标优化算法进行了测试对比，实验结果表明所提算法对于高维多目标优化问题具有较好的寻优能力，且该算法在30个测试问题中的14个测试问题上得到的优化结果均优于其他4个对比算法，特别是对于退化问题具有一定的寻优优势。
In the reference vector based high-dimensional many-objective evolutionary algorithms， the random selection of parent individuals will slow down the speed of convergence， and the lack of individuals assigned to some reference vectors will weaken the diversity of population. In order to solve these problems， an Improved high-dimensional Many-Objective Evolutionary Algorithm based on Decomposition （IMaOEA/D） was proposed. Firstly， when a reference vector was assigned at least two individuals in the framework of decomposition strategy， the parent individuals were selected for reproduction of offspring according to the distance from the individual assigned to the reference vector to the ideal point， so as to increase the search speed. Then， for the reference vector that was not assigned at least two individuals， the point with the smallest distance from the ideal point along the reference vector was selected from all the individuals， so that at least two individuals and the reference vector were associated. Meanwhile， by guaranteeing one individual was related to each reference vector after environmental selection， the diversity of population was ensured. The proposed method was tested and compared with other four high-dimensional many-objective optimization algorithms based on decomposition on the MaF test problem sets with 10 and 15 objectives. Experimental results show that， the proposed algorithm has good optimization ability for high-dimensional many-objective optimization problems： the optimization results of the proposed algorithm on 14 test problems of the 30 test problems are better than those of the other four comparison algorithms. Especially， the proposed algorithm has certain advantage on the degradation problem optimization. {{custom_citation.content}}
{{custom_citation.annotation}}
|
| [29] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [30] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [31] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [32] |
肖婧, 毕晓君, 王科俊. 基于全局排序的高维多目标优化研究[J]. 软件学报, 2015, 26(7): 1574-1583.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [33] |
刘超, 贺利军, 朱光宇. 基于熵和隶属度函数的高维多目标优化问题求解[J]. 计算机工程, 2016, 42(6): 185-190, 195.
为求解高维多目标优化问题,提出一种新的适应度分配策略,即模糊关联熵方法(FREM)。结合模糊信息熵理论和隶属度函数给出FREM,采用隶属度函数将Pareto解和理想解映射为模糊集,运用模糊信息熵理论处理Pareto解模糊集与理想解模糊集之间的内在关系,并进行适应度分配。以模糊关联熵系数引导群体智能算法进化。在DTLZ测试函数集上的实验结果表明,FREM能够解决高维多目标优化问题,避免子目标数量增加对算法的影响,并得到比随机权重法和NSGA-II更好的优化效果。
A new fitness assignment strategy is proposed for solving the high dmension multi-objective optimization problem,which is the Fuzzy Relevance Entropy Method(FREM).The FREM is built by integrating the fuzzy information entropy theory and the membership function.The membership function is used for transforming the ideal solution and the Pareto solution into fuzzy set.The fuzzy information entropy theory is used for calculating the internal relations between the ideal solution fuzzy set and the Pareto solution fuzzy set,meanwhile the fuzzy relevance entropy coefficient is used as the fitness value to guide the evolution of the swarm intelligence algorithm.Experimental test is conducted on DTLZ test function set.The results show that FREM can solve the high dmension multi-objective optimization problem,avoid the influence of the increasing number of the sub-objectives on the algorithm,and the solutions are better than those of random weighting method and NSGA-II.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [34] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [35] |
胡成玉, 余果, 代立国, 等. 知识驱动的高维多目标优化算法研究[J]. 华中科技大学学报(自然科学版), 2020, 48(6): 19-25.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [36] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [37] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [38] |
魏蛟龙, 岑朝辉. 基于蚁群算法的区域覆盖卫星星座优化设计[J]. 通信学报, 2006, 27(8): 62-66.
首先建立了以区域覆盖性能为目标的卫星星座参数优化模型，通过在连续域定义新的蚂蚁分类与转移策略改进了蚁群算法，使算法能够兼顾多目标优化时目标不兼容的情况，给出了基于该算法实现星座参数优化的框架。优化仿真实验验证了该算法的有效性，可为星座方案决策提供有力的支持。
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [39] |
姜兴龙, 姜泉江, 刘会杰, 等. 采用改进非支配近邻免疫算法的低轨混合星座设计优化[J]. 宇航学报, 2014, 35(9): 1 007-1 014.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [40] |
王浩, 张占月, 张海涛, 等. 基于改进MOPSO算法的区域侦察弹性星座重构方法[J]. 中国空间科学技术, 2021, 41(2): 86-95.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [41] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [42] |
周冲. 基于参考点的高维多目标演化算法研究及其在卫星星座设计中的应用[D]. 武汉: 中国地质大学, 2018.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| [43] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
| {{custom_ref.label}} |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
/
| 〈 |
|
〉 |
