An Improved Artificial Bee Colony Algorithm with History Best Points

被引：0

作者：

Xia, Xingyu ^{[1
]}

Wang, Xi ^{[2
]}

Hu, Haidong ^{[3
]}

Wu, Dongmei ^{[2
]}

Gao, Hao ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210042, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210042, Peoples R China

[3] Beijing Inst Control Engn, Beijing 10080, Peoples R China

来源：

PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年

关键词：

Artificial bee colony; convergence rate; evolutionary computation; global search ability; OPTIMIZATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Depending on the power global search ability, artificial bee colony algorithm attracts more attentions in recent years. But its slow convergence rate constraints its development. To better balance its exploration and exploitation abilities, we define a new point named as mean history best points (MHB) to lead the direction of bee population. The numerical experiments on the basic benchmark functions validate the efficiency of our algorithm.

引用

页码：2353 / 2358

页数：6

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