Fruit fly optimization algorithm based on adaptive search and cloud escape

被引：0

作者：

Zhang S. ^{[1
]}

Chen Y. ^{[1
]}

机构：

[1] School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, Jiangxi

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2018年 / 46卷 / 09期

关键词：

Adaptive search; Cloud escape; Fruit fly optimization algorithm; Global optimization; Local optimum; Premature convergence;

D O I：

10.13245/j.hust.180908

中图分类号：

学科分类号：

摘要：

In order to solve the premature convergence problem of fruit fly optimization algorithm in high dimensional complex problems,fruit fly optimization algorithm based on adaptive search and cloud escape was proposed.Considering the constant step of fruit fly optimization algorithm will affect the optimization accuracy,the iterative step value of the algorithm was used as the guiding factor to design the adaptive search method,which can coordinate the global search and local search.At the end of the algorithm search,in order to avoid the premature loss of population diversity,which lead to solution trapped in the local optimal environment,the cloud escape mechanism was designed based on the cloud model to help the algorithm to jump out of the local limit.Experiments on 10 different optimization problems show that the new algorithm has better performance in terms of accuracy,convergence speed and stability. © 2018, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：40 / 44and51

页数：4411

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