摘要
针对NSGA-Ⅱ算法中的模拟二进制交叉(SBX)算子以及NSGA-Ⅱ在收敛速度及多样性保持方面性能的不足,将反向学习机制(OBL)应用到NSGA-Ⅱ的初始化和进化过程中,并引入一种改进的算术交叉算子。ZDT系列测试函数在收敛性和多样性两个方面的评价结果表明,改进的NSGA-Ⅱ算法在收敛速度、收敛性和多样性上优于NSGA-Ⅱ算法。将改进的NSGA-Ⅱ算法应用于卫星星座优化设计中,仿真结果表明改进的算法在卫星星座优化设计中比较有效。
In order to overcome the shortages of Simulated Binary Crossover(SBX)operator, convergence speed and population diversity of NSGA-Ⅱ, this paper applies the opposition-based learning mechanism to the initializa- tion and evolution process of NSGA-Ⅱ algorithm. In addition, the paper introduces an improved arithmetic cross-over operator as well. The convergence and diversity of the proposed algorithm on the series of ZDT test bench-marks are evaluated and the results show that the improved NSGA-Ⅱ algorithm is better than the traditional NSGA-Ⅱ on converge speed, convergence and diversity. The paper applies the proposed algorithm to the optimization of satellite constellation design and the results indicate that the improved algorithm is very effective on this application.
出处
《计算机工程与应用》
CSCD
2012年第10期47-53,共7页
Computer Engineering and Applications
基金
国家自然科学基金项目(No.60873107)
关键词
多目标优化
NSGA-Ⅱ算法
反向学习
卫星星座
multi-objective optimization
NSGA-Ⅱ
opposition-based learning
satellite constellation
