基于正态云模型的自适应果蝇优化算法  

Adaptive fruit fly optimization algorithm based on normal cloud model

作　　者：崔金玲[1] 吴迪[2] 

机构地区：[1]安阳师范学院计算机与信息工程学院,河南安阳455000 [2]河南师范大学计算机与信息工程学院,河南新乡453007

出　　处：《河南理工大学学报（自然科学版）》2016年第5期697-705,共9页Journal of Henan Polytechnic University（Natural Science）

基　　金：河南省基础与前沿技术研究项目(122300410353;142300410084);教师教育精品资源共享课程项目(河南省教育厅教师〔2013〕136号)

摘　　要：为有效解决果蝇优化算法易陷入局部最优和收敛精度低等问题,提出一种采用正态云模型优化的自适应果蝇优化算法。该算法首先给出敏感因子的概念,采用自适应机制来修正敏感因子,控制搜索步长,更新果蝇种群位置;然后采用正态云模型描述味道浓度参数的随机性与模糊性,动态调整味道浓度参数,进行嗅觉搜索操作。最后将该算法应用于自动组卷中,与相关文献中的果蝇优化算法进行实验比较分析。结果表明,该算法在组卷效率及寻优精度上均有所提高。To solve the problem of fruit fly optimization algorithm（FOA） effectively,which is easy to trap in local optimal and the low convergence accuracy,an adaptive fruit fly optimization algorithm is proposed based on normal cloud model（CAFOA）.Firstly,the conception of sensitive factoris introduced and adjusted adaptively for controlling search step to update location.Then,the randomness and fuzziness of smell concentration parameter is described by normal cloud model and adjusted to finish osphresis search operation automatically,and the detailed steps of CAFOA are given.Finally,this algorithm is used to the automatic test,compared and analyzed with the experiment of other FOA in reference literatures.The results of experiment show that this algorithm has better advantages of test efficiency and accuracy.

关 键 词：果蝇优化算法 正态云模型 自适应 自动组卷 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]