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Mathematics 2019, 7(3), 289; https://doi.org/10.3390/math7030289

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

1,2, 3, 1,2,*, 1,2, 1,2, 1,2 and 1,2
1
School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2
Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China
3
School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
*
Author to whom correspondence should be addressed.
Received: 19 February 2019 / Revised: 12 March 2019 / Accepted: 13 March 2019 / Published: 21 March 2019
(This article belongs to the Special Issue Evolutionary Computation)
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PDF [898 KB, uploaded 21 March 2019]
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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. View Full-Text
Keywords: Artificial bee colony; swarm intelligence; elite strategy; dimension learning; global optimization Artificial bee colony; swarm intelligence; elite strategy; dimension learning; global optimization
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Xiao, S.; Wang, W.; Wang, H.; Tan, D.; Wang, Y.; Yu, X.; Wu, R. An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning. Mathematics 2019, 7, 289.

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