An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning
Songyi Xiao,
Wenjun Wang,
Hui Wang,
Dekun Tan,
Yun Wang,
Xiang Yu and
Runxiu Wu
Additional contact information
Songyi Xiao: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Wenjun Wang: School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
Hui Wang: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Dekun Tan: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Yun Wang: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Xiang Yu: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Runxiu Wu: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Mathematics, 2019, vol. 7, issue 3, 1-17
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.
Keywords: Artificial bee colony; swarm intelligence; elite strategy; dimension learning; global optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
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