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- Title
An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning.
- Authors
Xiao, Songyi; Wang, Wenjun; Wang, Hui; Tan, Dekun; Wang, Yun; Yu, Xiang; Wu, Runxiu
- Abstract
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.
- Subjects
BEES algorithm; AMERICAN Broadcasting Co.; BEE colonies; LEARNING strategies; GLOBAL optimization; SWARM intelligence; BEES
- Publication
Mathematics (2227-7390), 2019, Vol 7, Issue 3, p289
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math7030289