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Hybrid krill herd algorithm with differential evolution for global numerical optimization

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Abstract

In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.

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Authors and Affiliations

  1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China

    Gai-Ge Wang & Guo-Sheng Hao

  2. Department of Civil Engineering, The University of Akron, Akron, OH, 44325, USA

    Amir H. Gandomi

  3. Department of Civil and Environmental Engineering, Michigan State University, Engineering Building, East Lansing, MI, 48824, USA

    Amir H. Alavi

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  1. Gai-Ge Wang
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  2. Amir H. Gandomi
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  4. Guo-Sheng Hao
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Correspondence to Gai-Ge Wang.

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Wang, GG., Gandomi, A.H., Alavi, A.H. et al. Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput & Applic 25, 297–308 (2014). https://doi.org/10.1007/s00521-013-1485-9

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