EMOEA/D-DE算法在卫星有效载荷配置中的应用

doi:10.11772/j.issn.1001-9081.2014.08.2424

计算机应用 ›› 2014, Vol. 34 ›› Issue (8): 2424-2428.DOI: 10.11772/j.issn.1001-9081.2014.08.2424

EMOEA/D-DE算法在卫星有效载荷配置中的应用

李晖,袁文兵,熊慕舟

中国地质大学(武汉) 计算机学院，武汉430074

收稿日期:2014-03-04 修回日期:2014-04-18 出版日期:2014-08-01 发布日期:2014-08-10
通讯作者: 袁文兵
作者简介:李晖（1967-），女，湖北武汉人，教授，博士，主要研究方向：智能计算、智能信息处理；袁文兵(1989-)，男，江西新余人，硕士研究生，主要研究方向：智能计算、智能信息处理；熊慕舟（1980-），男，湖北武汉人，副教授，博士，主要研究方向：复杂系统建模与仿真、高性能计算。
基金资助:
国家自然科学基金资助项目;国家航天支撑基金资助项目

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

LI Hui,YUAN Wenbing,XIONG Muzhou

School of Computer Science, China University of Geosciences (Wuhan), Wuhan Hubei 430074, China

Received:2014-03-04 Revised:2014-04-18 Online:2014-08-01 Published:2014-08-10
Contact: YUAN Wenbing

摘要/Abstract

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。

Abstract:

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.

中图分类号:

TP391.4

李晖袁文兵熊慕舟. EMOEA/D-DE算法在卫星有效载荷配置中的应用[J]. 计算机应用, 2014, 34(8): 2424-2428.

LI Hui YUAN Wenbing XIONG Muzhou. Application of enhanced multi-objective evolutionary algorithm based on decomposition with differential evolution in configuration of satellite payload[J]. Journal of Computer Applications, 2014, 34(8): 2424-2428.

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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

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