基于云免疫算法的认知无线网络参数优化

发布时间：2018-04-06 22:14

本文选题：云模型　切入点：免疫克隆算法　出处：《计算机应用》2014年03期

【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。
[Abstract]:In order to improve the optimization effect of cognitive wireless network, a cognitive engine parameter adjustment algorithm based on immune optimization is proposed.Immune clone optimization is an effective intelligent optimization algorithm, which is suitable to solve the engine parameter adjustment problem of cognitive wireless networks.In immune optimization, mutation probability affects the search ability of the algorithm, and an adaptive mutation probability adjustment method based on cloud model is proposed, which makes use of the randomness and stable tendency of cloud droplets in normal cloud model.And it is used to optimize the parameters of cognitive wireless network.The algorithm is simulated in multi-carrier environment.The results show that the proposed algorithm converges quickly, and the parameter adjustment results are consistent with the preference of the objective function, and the parameters of the cognitive engine can be optimized.
【作者单位】：河南财经政法大学计算机与信息工程学院;郑州师范学院信息科学与技术学院;
【基金】：国家自然科学基金资助项目(61202285) 河南省教育厅自然科学研究项目(2A520004)
【分类号】：TN925

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