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Hybrid Swarming Algorithm With Van Der Waals Force

This paper proposes a hybrid swarming algorithm based on Ant Colony Optimization and Physarum Polycephalum Algorithm. And the Van Der Waals force is first applied to the pheromone update mechanism of the hybrid algorithm. The improved method can prevent premature convergence into the local optimal s...

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Detalles Bibliográficos
Autores principales: Yi, Zhang, Hongda, Yu, Mengdi, Sun, Yong, Xu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904736/
https://www.ncbi.nlm.nih.gov/pubmed/35284405
http://dx.doi.org/10.3389/fbioe.2022.806177
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author Yi, Zhang
Hongda, Yu
Mengdi, Sun
Yong, Xu
author_facet Yi, Zhang
Hongda, Yu
Mengdi, Sun
Yong, Xu
author_sort Yi, Zhang
collection PubMed
description This paper proposes a hybrid swarming algorithm based on Ant Colony Optimization and Physarum Polycephalum Algorithm. And the Van Der Waals force is first applied to the pheromone update mechanism of the hybrid algorithm. The improved method can prevent premature convergence into the local optimal solution. Simulation results show the proposed approach has excellent in solving accuracy and convergence time. We also compare the improved algorithm with other advanced algorithms and the results show that our algorithm is more accurate than the literature algorithms. In addition, we use the capitals of 35 Asian countries as an example to verify the robustness and versatility of the hybrid algorithm.
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spelling pubmed-89047362022-03-10 Hybrid Swarming Algorithm With Van Der Waals Force Yi, Zhang Hongda, Yu Mengdi, Sun Yong, Xu Front Bioeng Biotechnol Bioengineering and Biotechnology This paper proposes a hybrid swarming algorithm based on Ant Colony Optimization and Physarum Polycephalum Algorithm. And the Van Der Waals force is first applied to the pheromone update mechanism of the hybrid algorithm. The improved method can prevent premature convergence into the local optimal solution. Simulation results show the proposed approach has excellent in solving accuracy and convergence time. We also compare the improved algorithm with other advanced algorithms and the results show that our algorithm is more accurate than the literature algorithms. In addition, we use the capitals of 35 Asian countries as an example to verify the robustness and versatility of the hybrid algorithm. Frontiers Media S.A. 2022-02-23 /pmc/articles/PMC8904736/ /pubmed/35284405 http://dx.doi.org/10.3389/fbioe.2022.806177 Text en Copyright © 2022 Yi, Hongda, Mengdi and Yong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Yi, Zhang
Hongda, Yu
Mengdi, Sun
Yong, Xu
Hybrid Swarming Algorithm With Van Der Waals Force
title Hybrid Swarming Algorithm With Van Der Waals Force
title_full Hybrid Swarming Algorithm With Van Der Waals Force
title_fullStr Hybrid Swarming Algorithm With Van Der Waals Force
title_full_unstemmed Hybrid Swarming Algorithm With Van Der Waals Force
title_short Hybrid Swarming Algorithm With Van Der Waals Force
title_sort hybrid swarming algorithm with van der waals force
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904736/
https://www.ncbi.nlm.nih.gov/pubmed/35284405
http://dx.doi.org/10.3389/fbioe.2022.806177
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