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

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  • Songyi Xiao

    (School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, 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
    Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China)

  • Dekun Tan

    (School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China)

  • Yun Wang

    (School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China)

  • Xiang Yu

    (School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China)

  • Runxiu Wu

    (School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China)

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.

Suggested Citation

  • Songyi Xiao & Wenjun Wang & Hui Wang & Dekun Tan & Yun Wang & Xiang Yu & Runxiu Wu, 2019. "An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning," Mathematics, MDPI, vol. 7(3), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:3:p:289-:d:215860
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    Cited by:

    1. Yanhong Feng & Hongmei Wang & Zhaoquan Cai & Mingliang Li & Xi Li, 2023. "Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems," Mathematics, MDPI, vol. 11(8), pages 1-28, April.

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