Journal
MATHEMATICS
Volume 7, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/math7030289
Keywords
Artificial bee colony; swarm intelligence; elite strategy; dimension learning; global optimization
Categories
Funding
- National Natural Science Foundation of China [61663028, 61703199]
- Distinguished Young Talents Plan of Jiang-xi Province [20171BCB23075]
- Natural Science Foundation of Jiang-xi Province [20171BAB202035]
- Science and Technology Plan Project of Jiangxi Provincial Education Department [GJJ170994, GJJ180940]
- Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing [2016WICSIP015]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available