Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method

Hiqmet Kamberaj

doi:doi:10.11648/j.eas.20160102.12

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Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method

Hiqmet Kamberaj

Published in Engineering and Applied Sciences (Volume 1, Issue 2)

Received: 15 June 2015 Accepted: 1 July 2015 Published: 16 July 2016

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Abstract

We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum. The basis of the algorithm is to run multiple copies of independent swarms at different values of q parameter. Based on an energy criterion, chosen to satisfy the detailed balance, we are swapping the particle coordinates of neighboring swarms at regular iteration intervals. The swarm replicas with high q values are characterized by high diversity of particles allowing escaping local minima faster, while the low q replicas, characterized by low diversity of particles, are used to sample more efficiently the local basins. We compared the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms of ability to convergence faster to the global minimum. In additional, we also provide a method for optimally allocating the swarm replicas among different q values. Our algorithm is tested for three benchmark functions, which are known to be multimodal problems, at different dimensionalities.

Published in	Engineering and Applied Sciences (Volume 1, Issue 2)
DOI	10.11648/j.eas.20160102.12
Page(s)	20-25
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Swarm Quantum Particle, q-Gaussian Distribution, Global Optimization, Replica Exchange

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APA Style

Hiqmet Kamberaj. (2016). Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method. Engineering and Applied Sciences, 1(2), 20-25. https://doi.org/10.11648/j.eas.20160102.12

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ACS Style

Hiqmet Kamberaj. Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method. Eng. Appl. Sci. 2016, 1(2), 20-25. doi: 10.11648/j.eas.20160102.12

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AMA Style

Hiqmet Kamberaj. Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method. Eng Appl Sci. 2016;1(2):20-25. doi: 10.11648/j.eas.20160102.12

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@article{10.11648/j.eas.20160102.12,
  author = {Hiqmet Kamberaj},
  title = {Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method},
  journal = {Engineering and Applied Sciences},
  volume = {1},
  number = {2},
  pages = {20-25},
  doi = {10.11648/j.eas.20160102.12},
  url = {https://doi.org/10.11648/j.eas.20160102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20160102.12},
  abstract = {We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum. The basis of the algorithm is to run multiple copies of independent swarms at different values of q parameter. Based on an energy criterion, chosen to satisfy the detailed balance, we are swapping the particle coordinates of neighboring swarms at regular iteration intervals. The swarm replicas with high q values are characterized by high diversity of particles allowing escaping local minima faster, while the low q replicas, characterized by low diversity of particles, are used to sample more efficiently the local basins. We compared the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms of ability to convergence faster to the global minimum. In additional, we also provide a method for optimally allocating the swarm replicas among different q values. Our algorithm is tested for three benchmark functions, which are known to be multimodal problems, at different dimensionalities.},
 year = {2016}
}

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TY  - JOUR
T1  - Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method
AU  - Hiqmet Kamberaj
Y1  - 2016/07/16
PY  - 2016
N1  - https://doi.org/10.11648/j.eas.20160102.12
DO  - 10.11648/j.eas.20160102.12
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 20
EP  - 25
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20160102.12
AB  - We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum. The basis of the algorithm is to run multiple copies of independent swarms at different values of q parameter. Based on an energy criterion, chosen to satisfy the detailed balance, we are swapping the particle coordinates of neighboring swarms at regular iteration intervals. The swarm replicas with high q values are characterized by high diversity of particles allowing escaping local minima faster, while the low q replicas, characterized by low diversity of particles, are used to sample more efficiently the local basins. We compared the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms of ability to convergence faster to the global minimum. In additional, we also provide a method for optimally allocating the swarm replicas among different q values. Our algorithm is tested for three benchmark functions, which are known to be multimodal problems, at different dimensionalities.
VL  - 1
IS  - 2
ER  -

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Author Information

Hiqmet Kamberaj

Faculty of Engineering, Department of Computer Engineering, International Balkan University, Skopje, R. of Macedonia

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APA Style

Hiqmet Kamberaj. (2016). Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method. Engineering and Applied Sciences, 1(2), 20-25. https://doi.org/10.11648/j.eas.20160102.12

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ACS Style

Hiqmet Kamberaj. Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method. Eng. Appl. Sci. 2016, 1(2), 20-25. doi: 10.11648/j.eas.20160102.12

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AMA Style

Hiqmet Kamberaj. Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method. Eng Appl Sci. 2016;1(2):20-25. doi: 10.11648/j.eas.20160102.12

Copy | Download

@article{10.11648/j.eas.20160102.12,
  author = {Hiqmet Kamberaj},
  title = {Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method},
  journal = {Engineering and Applied Sciences},
  volume = {1},
  number = {2},
  pages = {20-25},
  doi = {10.11648/j.eas.20160102.12},
  url = {https://doi.org/10.11648/j.eas.20160102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20160102.12},
  abstract = {We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum. The basis of the algorithm is to run multiple copies of independent swarms at different values of q parameter. Based on an energy criterion, chosen to satisfy the detailed balance, we are swapping the particle coordinates of neighboring swarms at regular iteration intervals. The swarm replicas with high q values are characterized by high diversity of particles allowing escaping local minima faster, while the low q replicas, characterized by low diversity of particles, are used to sample more efficiently the local basins. We compared the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms of ability to convergence faster to the global minimum. In additional, we also provide a method for optimally allocating the swarm replicas among different q values. Our algorithm is tested for three benchmark functions, which are known to be multimodal problems, at different dimensionalities.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method
AU  - Hiqmet Kamberaj
Y1  - 2016/07/16
PY  - 2016
N1  - https://doi.org/10.11648/j.eas.20160102.12
DO  - 10.11648/j.eas.20160102.12
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 20
EP  - 25
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20160102.12
AB  - We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum. The basis of the algorithm is to run multiple copies of independent swarms at different values of q parameter. Based on an energy criterion, chosen to satisfy the detailed balance, we are swapping the particle coordinates of neighboring swarms at regular iteration intervals. The swarm replicas with high q values are characterized by high diversity of particles allowing escaping local minima faster, while the low q replicas, characterized by low diversity of particles, are used to sample more efficiently the local basins. We compared the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms of ability to convergence faster to the global minimum. In additional, we also provide a method for optimally allocating the swarm replicas among different q values. Our algorithm is tested for three benchmark functions, which are known to be multimodal problems, at different dimensionalities.
VL  - 1
IS  - 2
ER  -

Copy | Download