Adaptive Structure-Redesigned-Based Bacterial Foraging Optimization

被引：0

作者：

Tan, L. J. ^{[1
]}

Yi, W. J. ^{[2
]}

Yang, C. ^{[2
]}

Feng, Y. Y. ^{[2
]}

机构：

[1] Shenzhen Inst Informat Technol, Dept Business Management, Shenzhen 518172, Peoples R China

[2] Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2016, PT II | 2016年 / 9772卷

关键词：

Adaptive adjustment; Structure-redesigned-based bacterial foraging optimization (SRBFO); ASRBFO;

D O I：

10.1007/978-3-319-42294-7_80

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes an adaptive structure-redesigned-based bacterial foraging optimization called ASRBFO. In this improved algorithm, the chemotaxis step of SRBFO is adaptively adjusted based on the bacterial searching status. The personal current and best positions of bacteria as well as the mean of all bacterial positions are taken and used to calculate the chemotaxis step during the searching process. The goal of the study is to improve the convergence efficiency and the accuracy of SRBFO. To demonstrate the performance, six different benchmark functions are chosen to the experiment, and other three SRBFOs are used to compare with the proposed algorithm. The results show that ASRBFO outperforms other SRBFOs.

引用

页码：897 / 907

页数：11

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