Convergence Analysis of Hybrid Free Search and Invasive Weed Optimization Algorithm

被引：1
|
作者：
Li, Lu [1 ]
Wang, Xingyu [1 ]
Zhang, Zihou [2 ]
Fan, Liubin [2 ]
机构：
[1] East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China
[2] Shanghai Univ Engn Sci, Fundamental Stud Sch, Shanghai, Peoples R China
来源：
ELECTRICAL INFORMATION AND MECHATRONICS AND APPLICATIONS, PTS 1 AND 2 | 2012年 / 143-144卷
关键词：
Free search; Weed optimization algorithm; Convergence; Shubert function;
D O I：
10.4028/www.scientific.net/AMM.143-144.329
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Considering the fitness of each individual, a hybrid intelligence algorithm is established, which combine the excellent probing ability of free search algorithm (FS) with exploiting ability of invasive weed optimization algorithm (IWO). The hybrid algorithm can overcome the disadvantage of lower optimization rate in late evolution for FS and taking advantage of powerful exploiting abilities for IWO. Identity between FS and IWO is analyzed and convergence of the two algorithms in solving continuous function optimization is provided. Simulations confirmed the analysis. Multi-model Shubert function is chosen to carry out the simulation. Compared with FS and IWO, the hybrid algorithm is superior in convergence speed and robustness.
引用
收藏
页码：329 / +
页数：3
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