Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative

Qiao He; Zhang Yuhan

doi:doi:10.11648/j.sd.20221006.26

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Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative

Qiao He, Zhang Yuhan

Published in Science Discovery (Volume 10, Issue 6)

Received: 5 November 2022 Accepted: 29 November 2022 Published: 8 December 2022

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Abstract

The basic sand cat swarm optimization (SCSO) algorithm has the shortcomings of low accuracy, slow convergence and easy local optimality in solving complex optimization problems. An improved SCSO algorithm (Improved sand cat swarm optimization, ISCSO) is proposed based on chaotic sequences and Lévy flight. The initial population is generated using the Kent chaos strategy, which reduces the overlapping probability of individual distribution within the sand cat population and increases the diversity of the initial sand cat population. The auditory sensitivity of the sand cat is improved to balance the process of sand cat development and exploration, increasing the search range of the algorithm while improving the convergence speed. The introduction of Lévy flight trajectory change perturbs the sand cat position, updates the position formula, improves the situation of the sand cat falling into local stagnation, and improves the algorithm in the iterative process of premature and falling into the local optimum. Through the test of the benchmark function, comparing the optimal value, the mean value, the standard deviation and the Wilcoxon rank sum test under different optimization algorithms, the statistical results that the improved SCSO algorithm has higher accuracy in finding the best and better convergence. Finally, ISCSO is used to optimize the welded beam problem to further verify the effectiveness of the improved algorithm.

Published in	Science Discovery (Volume 10, Issue 6)
DOI	10.11648/j.sd.20221006.26
Page(s)	482-488
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), 2022. Published by Science Publishing Group

Keywords

Sand Cat Swarm Optimization, Kent Chaotic, Lévy Flight, Multi-strategy Cooperative, Parameter Optimization

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Qiao He, Zhang Yuhan. (2022). Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative. Science Discovery, 10(6), 482-488. https://doi.org/10.11648/j.sd.20221006.26

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ACS Style

Qiao He; Zhang Yuhan. Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative. Sci. Discov. 2022, 10(6), 482-488. doi: 10.11648/j.sd.20221006.26

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Qiao He, Zhang Yuhan. Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative. Sci Discov. 2022;10(6):482-488. doi: 10.11648/j.sd.20221006.26

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@article{10.11648/j.sd.20221006.26,
  author = {Qiao He and Zhang Yuhan},
  title = {Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative},
  journal = {Science Discovery},
  volume = {10},
  number = {6},
  pages = {482-488},
  doi = {10.11648/j.sd.20221006.26},
  url = {https://doi.org/10.11648/j.sd.20221006.26},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20221006.26},
  abstract = {The basic sand cat swarm optimization (SCSO) algorithm has the shortcomings of low accuracy, slow convergence and easy local optimality in solving complex optimization problems. An improved SCSO algorithm (Improved sand cat swarm optimization, ISCSO) is proposed based on chaotic sequences and Lévy flight. The initial population is generated using the Kent chaos strategy, which reduces the overlapping probability of individual distribution within the sand cat population and increases the diversity of the initial sand cat population. The auditory sensitivity of the sand cat is improved to balance the process of sand cat development and exploration, increasing the search range of the algorithm while improving the convergence speed. The introduction of Lévy flight trajectory change perturbs the sand cat position, updates the position formula, improves the situation of the sand cat falling into local stagnation, and improves the algorithm in the iterative process of premature and falling into the local optimum. Through the test of the benchmark function, comparing the optimal value, the mean value, the standard deviation and the Wilcoxon rank sum test under different optimization algorithms, the statistical results that the improved SCSO algorithm has higher accuracy in finding the best and better convergence. Finally, ISCSO is used to optimize the welded beam problem to further verify the effectiveness of the improved algorithm.},
 year = {2022}
}

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TY  - JOUR
T1  - Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative
AU  - Qiao He
AU  - Zhang Yuhan
Y1  - 2022/12/08
PY  - 2022
N1  - https://doi.org/10.11648/j.sd.20221006.26
DO  - 10.11648/j.sd.20221006.26
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 482
EP  - 488
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20221006.26
AB  - The basic sand cat swarm optimization (SCSO) algorithm has the shortcomings of low accuracy, slow convergence and easy local optimality in solving complex optimization problems. An improved SCSO algorithm (Improved sand cat swarm optimization, ISCSO) is proposed based on chaotic sequences and Lévy flight. The initial population is generated using the Kent chaos strategy, which reduces the overlapping probability of individual distribution within the sand cat population and increases the diversity of the initial sand cat population. The auditory sensitivity of the sand cat is improved to balance the process of sand cat development and exploration, increasing the search range of the algorithm while improving the convergence speed. The introduction of Lévy flight trajectory change perturbs the sand cat position, updates the position formula, improves the situation of the sand cat falling into local stagnation, and improves the algorithm in the iterative process of premature and falling into the local optimum. Through the test of the benchmark function, comparing the optimal value, the mean value, the standard deviation and the Wilcoxon rank sum test under different optimization algorithms, the statistical results that the improved SCSO algorithm has higher accuracy in finding the best and better convergence. Finally, ISCSO is used to optimize the welded beam problem to further verify the effectiveness of the improved algorithm.
VL  - 10
IS  - 6
ER  -

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Author Information

Qiao He

Faulty of Electrical and Control Engineering, Liaoning Technical University, Huludao, China
Zhang Yuhan

Faulty of Electrical and Control Engineering, Liaoning Technical University, Huludao, China

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APA Style

Qiao He, Zhang Yuhan. (2022). Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative. Science Discovery, 10(6), 482-488. https://doi.org/10.11648/j.sd.20221006.26

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ACS Style

Qiao He; Zhang Yuhan. Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative. Sci. Discov. 2022, 10(6), 482-488. doi: 10.11648/j.sd.20221006.26

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AMA Style

Qiao He, Zhang Yuhan. Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative. Sci Discov. 2022;10(6):482-488. doi: 10.11648/j.sd.20221006.26

Copy | Download

@article{10.11648/j.sd.20221006.26,
  author = {Qiao He and Zhang Yuhan},
  title = {Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative},
  journal = {Science Discovery},
  volume = {10},
  number = {6},
  pages = {482-488},
  doi = {10.11648/j.sd.20221006.26},
  url = {https://doi.org/10.11648/j.sd.20221006.26},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20221006.26},
  abstract = {The basic sand cat swarm optimization (SCSO) algorithm has the shortcomings of low accuracy, slow convergence and easy local optimality in solving complex optimization problems. An improved SCSO algorithm (Improved sand cat swarm optimization, ISCSO) is proposed based on chaotic sequences and Lévy flight. The initial population is generated using the Kent chaos strategy, which reduces the overlapping probability of individual distribution within the sand cat population and increases the diversity of the initial sand cat population. The auditory sensitivity of the sand cat is improved to balance the process of sand cat development and exploration, increasing the search range of the algorithm while improving the convergence speed. The introduction of Lévy flight trajectory change perturbs the sand cat position, updates the position formula, improves the situation of the sand cat falling into local stagnation, and improves the algorithm in the iterative process of premature and falling into the local optimum. Through the test of the benchmark function, comparing the optimal value, the mean value, the standard deviation and the Wilcoxon rank sum test under different optimization algorithms, the statistical results that the improved SCSO algorithm has higher accuracy in finding the best and better convergence. Finally, ISCSO is used to optimize the welded beam problem to further verify the effectiveness of the improved algorithm.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Improved Sand Cat Swarm Optimization with Multi-strategy Cooperative
AU  - Qiao He
AU  - Zhang Yuhan
Y1  - 2022/12/08
PY  - 2022
N1  - https://doi.org/10.11648/j.sd.20221006.26
DO  - 10.11648/j.sd.20221006.26
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 482
EP  - 488
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20221006.26
AB  - The basic sand cat swarm optimization (SCSO) algorithm has the shortcomings of low accuracy, slow convergence and easy local optimality in solving complex optimization problems. An improved SCSO algorithm (Improved sand cat swarm optimization, ISCSO) is proposed based on chaotic sequences and Lévy flight. The initial population is generated using the Kent chaos strategy, which reduces the overlapping probability of individual distribution within the sand cat population and increases the diversity of the initial sand cat population. The auditory sensitivity of the sand cat is improved to balance the process of sand cat development and exploration, increasing the search range of the algorithm while improving the convergence speed. The introduction of Lévy flight trajectory change perturbs the sand cat position, updates the position formula, improves the situation of the sand cat falling into local stagnation, and improves the algorithm in the iterative process of premature and falling into the local optimum. Through the test of the benchmark function, comparing the optimal value, the mean value, the standard deviation and the Wilcoxon rank sum test under different optimization algorithms, the statistical results that the improved SCSO algorithm has higher accuracy in finding the best and better convergence. Finally, ISCSO is used to optimize the welded beam problem to further verify the effectiveness of the improved algorithm.
VL  - 10
IS  - 6
ER  -

Copy | Download