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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

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  • ReceivedMay 11, 2018
  • AcceptedSep 5, 2018
  • PublishedSep 10, 2019

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

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  • Figure 1

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

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    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

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