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A hybrid method based on krill herd and quantum-behaved particle swarm optimization

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Abstract

A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH–QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH–QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH–QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.

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Authors and Affiliations

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

    Gai-Ge Wang

  2. Institute of Algorithm and Big Data Analysis, Northeast Normal University, Changchun, 130117, China

    Gai-Ge Wang

  3. School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China

    Gai-Ge Wang

  4. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, 48824, USA

    Amir H. Gandomi

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

    Amir H. Alavi

  6. Department of Computer Science and Engineering, Cambridge Institute of Technology, Cambridge Village, Tatisilwai, Ranchi, 835103, Jharkhand, India

    Suash Deb

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  1. Gai-Ge Wang
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  4. Suash Deb
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Correspondence to Gai-Ge Wang.

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Wang, GG., Gandomi, A.H., Alavi, A.H. et al. A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput & Applic 27, 989–1006 (2016). https://doi.org/10.1007/s00521-015-1914-z

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