EMOEA/D-DE算法在卫星有效载荷配置中的应用  

Application of enhanced multi-objective evolutionary algorithm based on decomposition with differential evolution in configuration of satellite payload

作　　者：李晖[1] 袁文兵[1] 熊慕舟[1] 

机构地区：[1]中国地质大学(武汉)计算机学院,武汉430074

出　　处：《计算机应用》2014年第8期2424-2428,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61103145);国家航天支撑基金资助项目(2012-HT-ZGDZDX)

摘　　要：针对卫星有效载荷配置问题,提出了一种基于差分进化分解的改进多目标优化算法(EMOEA/D-DE)的有效载荷配置模型。该模型将配置问题转化为以卫星数、卫星冗余度为目标的多目标优化问题(MOP),并采用EMOEA/D-DE进行求解。此外,针对随机均匀初始化会导致种群在目标空间分布过于集中的问题,采用与优化目标相结合的随机初始化方法进行改进。实验结果表明,该模型所求解集的平均差异性在0.05以内,分布度值在0.9以上,具有较好的稳定性及分布性,且改进后的算法收敛速度提升近1倍,所求解的近似Pareto前沿相对更优。To solve the satellite payload configuration problem, a satellite payload configuration model based on Enhanced Multi-Objective Evolutionary Algorithm based on Decomposition with Differential Evolution （EMOEA/D-DE） algorithm was proposed. This model turned the configuration problem into a Multi-objective Optimization Problem （MOP）, which took the number of satellites and satellite redundancy as the optimization objectives, and solved it by using EMOEA/D-DE algorithm. Furthermore, to overcome the concentration of population＇s distribution in objective space resulted by the original randomly uniform initialization, a new random initialization combined with optimization objectives was introduced. The experimental results show that the solution set obtained by this model has good stability and distribution. The average difference is less than 0.05 and the distribution of value is above 0.9. Besides, the improved algorithm doubles the convergence speed nearly, and the approximation of Pareto front obtained is relatively better.

关 键 词：卫星有效载荷配置 多目标优化问题 MOEA D EMOEA D-DE 种群初始化 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]