混沌协方差矩阵自适应进化策略优化算法
发布时间：2019-07-12 14:43
【摘要】：为了改进进化策略算法的性能,提出了一种混沌协方差矩阵自适应进化策略(ChaosCMA-ES)算法,该算法在协方差矩阵自适应进化策略(CMA-ES)算法的基础上引入了混沌算子,并利用其更新种群中心的位置,使得种群具备良好的全局搜索能力。试验结果表明,本文算法对复杂多峰函数的寻优效果好于其他几种算法。最后,将本文算法用于优化网络安全态势的预测模型,预测结果的精度高于其他方法。
[Abstract]:In order to improve the performance of evolutionary strategy algorithm, a chaotic covariance matrix adaptive evolutionary strategy (ChaosCMA-ES) algorithm is proposed. Chaos operator is introduced on the basis of covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm, and it is used to update the position of population center, so that the population has good global search ability. The experimental results show that the optimization effect of the proposed algorithm for complex multipeak functions is better than that of other algorithms. Finally, the algorithm is applied to optimize the prediction model of network security situation, and the accuracy of the prediction results is higher than that of other methods.
【作者单位】： 哈尔滨理工大学计算机科学与技术学院;
【基金】：国家自然科学基金项目(61103149,61362016) 黑龙江省自然科学基金项目(QC2013C060)
【分类号】：TP18
本文编号：2513777
[Abstract]:In order to improve the performance of evolutionary strategy algorithm, a chaotic covariance matrix adaptive evolutionary strategy (ChaosCMA-ES) algorithm is proposed. Chaos operator is introduced on the basis of covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm, and it is used to update the position of population center, so that the population has good global search ability. The experimental results show that the optimization effect of the proposed algorithm for complex multipeak functions is better than that of other algorithms. Finally, the algorithm is applied to optimize the prediction model of network security situation, and the accuracy of the prediction results is higher than that of other methods.
【作者单位】： 哈尔滨理工大学计算机科学与技术学院;
【基金】：国家自然科学基金项目(61103149,61362016) 黑龙江省自然科学基金项目(QC2013C060)
【分类号】：TP18
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