Abstract: In order to improve the parameter optimization results of cognitive wireless network, an immune optimization based parameter adjustment algorithm was proposed. Engine parameter adjustment of cognitive wireless network is a multi-objective optimization problem. Intelligent optimization method is suitable for solving it. Immune clonal optimization is an effective intelligent optimization algorithm. The mutation probability affects the searching capabilities in immune optimization. Cloud droplets have randomness and stable tendency in normal cloud model, so an adaptive mutation probability adjustment method based on cloud model was proposed, and it was used in parameter optimization of cognitive radio networks. The simulation experiments were done to test the algorithm under multi-carrier system. The results show that, compared with relative algorithms, the proposed algorithm has better convergence, and the parameter adjustment results are consistent with the preferences for the objectives function. It can get optimal parameter results of cognitive engine.

Key words: cloud model, immune clone algorithm, cognitive wireless network, parameter optimization, mutation probability

摘要： 为了提高认知无线网络的参数优化效果,提出了一种基于免疫优化的认知引擎参数调整算法。免疫克隆优化是一种有效的智能优化算法,适合求解认知无线网络的引擎参数调整问题。免疫优化中,变异概率影响着算法的搜索能力;利用正态云模型云滴的随机性和稳定倾向性特点,提出了一种基于云模型的自适应变异概率调整方法,并用于认知无线网络的参数优化。在多载波环境下对算法进行了仿真实验。结果表明,所提算法收敛速度较快,参数调整结果与对目标函数的偏好一致,能够实现认知引擎参数优化。

关键词: 云模型, 免疫克隆算法, 认知无线网络, 参数优化, 变异概率