An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

被引：14

作者：

Xiao, Songyi ^{[1
,2
]}

Wang, Wenjun ^{[3
]}

Wang, Hui ^{[1
,2
]}

Tan, Dekun ^{[1
,2
]}

Wang, Yun ^{[1
,2
]}

Yu, Xiang ^{[1
,2
]}

Wu, Runxiu ^{[1
,2
]}

机构：

[1] Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Jiangxi, Peoples R China

[2] Nanchang Inst Technol, Jiangxi Prov Key Lab Water Informat Cooperat Sens, Nanchang 330099, Jiangxi, Peoples R China

[3] Nanchang Inst Technol, Sch Business Adm, Nanchang 330099, Jiangxi, Peoples R China

来源：

MATHEMATICS | 2019年 / 7卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Artificial bee colony; swarm intelligence; elite strategy; dimension learning; global optimization; PARTICLE SWARM OPTIMIZATION; KRILL HERD ALGORITHM; FIREFLY ALGORITHM;

D O I：

10.3390/math7030289

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

引用

页数：17