摘要： 针对多目标干扰多参数波形优化设计问题中需考虑参数过多、高维等问题，给出一种基于实数编码的改进混沌遗传算法。引入An混沌随机数生成器以提高混沌序列的均匀性和随机性，提出将遗传操作中交叉得到的优秀个体与新群体竞争，提高收敛速度。改进后，混沌遗传算法的收敛性和全局搜索能力都有所提高，通过仿真证明了该算法的有效性和正确性。

关键词: 多目标干扰, 混沌遗传算法, 混沌随机数生成器

Abstract: To solve the problem of multi-parameter waveform optimization during multipurpose jamming, a chaos genetic algorithm based on real-encoded is presented. In order to enhance the uniformity and randomization of chaos sequence, an chaotic random number generator is used in this algorithm for the first time. By competing excellent individuals obtained from crossover with new populations, the convergence speed is quickened. The modified chaotic genetic algorithm improves the convergence and search capability compared with normal genetic algorithm. The availability and validity of this method is verified through simulations.

Key words: multipurpose jamming, chaos genetic algorithm, chaotic random number generator

中图分类号: 

• TN972+2