logo

Journals Books
Advanced Search
Sign in Register

中文

SCIENCE CHINA Information Sciences
Download PDF
SCIENCE CHINA Information Sciences, Volume 63 , Issue 5 : 159201(2020) https://doi.org/10.1007/s11432-018-9618-2

Hybrid quantum particle swarm optimization algorithm and its application

Yukun WANG 1,2, Xuebo CHEN 2,*
More info
  • ReceivedMay 11, 2018
  • AcceptedSep 5, 2018
  • PublishedSep 10, 2019
Previous Table of Contents Next
Share
Rating

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 71571091, 71771112, 61473054).


Supplement

Appendixes A–E.


References

[1] Wang G G, Gandomi A H, Alavi A H. A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput Applic, 2016, 27: 989-1006 CrossRef Google Scholar

[2] Wu T, Yan Y S, Chen X. Improved dual-group interaction QPSO algorithm based on random evaluation. Control Decision, 2015, 3: 526-530, doi: 10.13195/j.kzyjc.2013.1291. Google Scholar

[3] Rehman O U, Yang J, Zhou Q. A modified QPSO algorithm applied to engineering inverse problems in electromagnetics. JAE, 2017, 54: 107-121 CrossRef Google Scholar

[4] Luo Q, Gong Y, Jia C. Stability of gene regulatory networks with Lévy noise. Sci China Inf Sci, 2017, 60: 072204 CrossRef Google Scholar

[5] Turgut O E. Hybrid Chaotic Quantum behaved Particle Swarm Optimization algorithm for thermal design of plate fin heat exchangers. Appl Math Model, 2016, 40: 50-69 CrossRef Google Scholar

[6] Zhao J, Fu Y, Mei J. An improved cooperative QPSO algorithm with adaptive mutation based on entire search history. Acta Electronica Sini, 2016, 44: 2900-2907, doi: 10.3969/j.issn.0372-2112.2016.12.013. Google Scholar

[7] Zhang J, Dolg M. ABCluster: the artificial bee colony algorithm for cluster global optimization. Phys Chem Chem Phys, 2015, 17: 24173-24181 CrossRef PubMed ADS Google Scholar

[8] Wang Q W, Li X Q, Chen H J, et al. The study of structures of gold clusters by artifical bee colony algorithm. J Atom Mol Phys, 2017, 34: 1040--1048. Google Scholar

  • Click to view Download high-quality image

    Figure 1

    (Color online) GSTs of Au($n$) $(n=12,\ldots,30)$ clusters achieved by HQPSO.

  •   

    Algorithm 1 HQPSO

    Require:The population size $M$, maximum iteration number $T$, search range $[X_{\rm~min},~X_{\rm~max}]$, step length factors $\lambda$ and parameter $L$;

    Output:Obtain the best solution $G$ and best fitness value Fg of the problem;

    $t=1$; initialize the population, calculate the fitness value of each particle, and record the personal best solution ${\rm~Pb}_{i}$ and its fitness ${\rm~Fb}_{i}$, the globe best solution $G$ and its fitness Fg;

    while $t<T$ do

    for $i$ to $M$

    if ${\rm~rand}<\psi$ ($\psi$ is calculated using Eq. (6)) then

    if ${\rm~rand}<0.5$ then

    Execute global search strategy using Eq. (2);

    else

    Execute local search strategy using Eq. (3);

    end if

    else

    Execute enhanced search strategy usingEq. (5);

    end if

    Calculate the fitness of each new particle; update ${\rm~Pb}_{i}$ and $~{\rm~Fb}_{i}$, $G$ and Fg by using greedy selection method;

    if fitness of ${\rm~Pb}_{i}$ has not updated in recent $L$ iterations then

    Execute hopping operation using Eq. (4);

    end if

    end for

    $t=t+1$;

    end while