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Neural Computing and Applications

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01.05.2016 | Original Article

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

verfasst von: Gai-Ge Wang, Amir H. Gandomi, Amir H. Alavi, Suash Deb

Erschienen in: Neural Computing and Applications | Ausgabe 4/2016

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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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Titel: A hybrid method based on krill herd and quantum-behaved particle swarm optimization
verfasst von: Gai-Ge Wang
Amir H. Gandomi
Amir H. Alavi
Suash Deb
Publikationsdatum: 01.05.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1914-z

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