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

Publication Type:
Journal Article
Neural Computing and Applications, 2016, 27, (4), pp. 989-1006
Issue Date:
Filename Description Size
Wang2016_Article_AHybridMethodBasedOnKrillHerdA.pdfPublished version1.03 MB
Adobe PDF
Full metadata record
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.
Please use this identifier to cite or link to this item: