Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation

Ting Xiang; Dazhi Pan; Haijie Pei

doi:doi:10.11648/j.ajsea.20160501.11

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Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation

Ting Xiang, Dazhi Pan, Haijie Pei

Published in American Journal of Software Engineering and Applications (Volume 5, Issue 1)

Received: 30 November 2015 Accepted: 10 December 2015 Published: 25 December 2015

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Abstract

In order to solve the vehicle routing problem, this paper introduces the Gauss mutation, which is based on the common particle swarm algorithm, to constitute an improved particle swarm algorithm (NPSO). In the process of solving vehicle routing problem, the NPSO is encoded by integer and proposes a new way to adjust the infeasible solutions. The particles are divided into two overlapping subgroups, and join the two-two exchange neighborhood search to iterate. Finally, the simulation experiments show that the proposed algorithm can get the optimal solution faster and better, and it has a certain validity and practicability.

Published in	American Journal of Software Engineering and Applications (Volume 5, Issue 1)
DOI	10.11648/j.ajsea.20160501.11
Page(s)	1-6
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), 2015. Published by Science Publishing Group

Keywords

Particle Swarm Optimization, Vehicle Routing Problem, Gauss Mutation, Neighborhood Search

References

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[2]	Laporte G. The vehicle routing problem: an overview of exact and approximation algorithms [J]. European Journal of operational Research, 1992, 5(9): 345一358.
[3]	Hongchun HU, Yaohua WU, Li LIAO. Optimization and application of logistics distribution vehicle [J]. Journal of Shandong University (Engineering Science), 2007, 37(4): 104-107.
[4]	Eberhart R, Kennedy J. A new optimizer using particle swarm theory [A]. Proceedings of the International Symposium on Micro Machine and Human Science [C]. Piscataway, NJ, USA: IEEE, 1995: 39-43.
[5]	Kennedy J, Eberhart R. Particle swarm optimization [A]. Proceedings of the IEEE International Conference on Neural Networks [C]. Piscataway, NJ, USA: IEEE, 1995: 1942-1948.
[6]	Zhen Huang. Hybrid quantum Particle Swarm Optimization algorithm for vehicle routing problem [J]. Computer Engineering and Applications, 2013. 49(24): 219-223.
[7]	Dongqing Ma, Wei Wang. Logistics distribution vehicle scheduling based on improved particle swarm optimization [J]. Computer Engineering and Applications, 2014, 50(11): 246-270.
[8]	Yaohua Wu, Nianzhi Zhang. Modified Particle Swarm Optimization algorithm for vehicle routing problem with time windows [J]. Computer Engineering and Applications, 2010, 46(15): 230-234.
[9]	Ya Li, Dan Li, Dong Wang. Improved chaos particle swarm optimization algorithm for vehicle routing problem [J]. Application Research of Computers, 2011, 28(11): 4107-4110.
[10]	Ning Li, Tong Zou. Particle swarm optimization for vehicle routing problem [J]. Journal of Systems Engineering, 2004, 19(6): 596-600.
[11]	Bing Wu. Research and application of particle swarm optimization algorithm for vehicle routing problem [D]. Zhejiang University of Technology, 2008.
[12]	Yuanbing Mo, Fuyong Liu. Artificial glowworm swarm optimization algorithm with Gauss mutation [J]. Application Research of Computers, 2013, 30(1): 121-123.
[13]	Xing Liu, Guoguang He. Study on tabu search algorithm for stochastic vehicle routing problem [J]. Application Research of Computers, 2007, 43(24): 179: 181.

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APA Style

Ting Xiang, Dazhi Pan, Haijie Pei. (2015). Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation. American Journal of Software Engineering and Applications, 5(1), 1-6. https://doi.org/10.11648/j.ajsea.20160501.11

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ACS Style

Ting Xiang; Dazhi Pan; Haijie Pei. Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation. Am. J. Softw. Eng. Appl. 2015, 5(1), 1-6. doi: 10.11648/j.ajsea.20160501.11

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AMA Style

Ting Xiang, Dazhi Pan, Haijie Pei. Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation. Am J Softw Eng Appl. 2015;5(1):1-6. doi: 10.11648/j.ajsea.20160501.11

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@article{10.11648/j.ajsea.20160501.11,
  author = {Ting Xiang and Dazhi Pan and Haijie Pei},
  title = {Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation},
  journal = {American Journal of Software Engineering and Applications},
  volume = {5},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajsea.20160501.11},
  url = {https://doi.org/10.11648/j.ajsea.20160501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20160501.11},
  abstract = {In order to solve the vehicle routing problem, this paper introduces the Gauss mutation, which is based on the common particle swarm algorithm, to constitute an improved particle swarm algorithm (NPSO). In the process of solving vehicle routing problem, the NPSO is encoded by integer and proposes a new way to adjust the infeasible solutions. The particles are divided into two overlapping subgroups, and join the two-two exchange neighborhood search to iterate. Finally, the simulation experiments show that the proposed algorithm can get the optimal solution faster and better, and it has a certain validity and practicability.},
 year = {2015}
}

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TY  - JOUR
T1  - Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation
AU  - Ting Xiang
AU  - Dazhi Pan
AU  - Haijie Pei
Y1  - 2015/12/25
PY  - 2015
N1  - https://doi.org/10.11648/j.ajsea.20160501.11
DO  - 10.11648/j.ajsea.20160501.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20160501.11
AB  - In order to solve the vehicle routing problem, this paper introduces the Gauss mutation, which is based on the common particle swarm algorithm, to constitute an improved particle swarm algorithm (NPSO). In the process of solving vehicle routing problem, the NPSO is encoded by integer and proposes a new way to adjust the infeasible solutions. The particles are divided into two overlapping subgroups, and join the two-two exchange neighborhood search to iterate. Finally, the simulation experiments show that the proposed algorithm can get the optimal solution faster and better, and it has a certain validity and practicability.
VL  - 5
IS  - 1
ER  -

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Author Information

Ting Xiang

College of Mathematic and Information, China West Normal University, Nanchong, China
Dazhi Pan

College of Mathematic and Information, China West Normal University, Nanchong, China
Haijie Pei

College of Mathematic and Information, China West Normal University, Nanchong, China

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APA Style

Ting Xiang, Dazhi Pan, Haijie Pei. (2015). Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation. American Journal of Software Engineering and Applications, 5(1), 1-6. https://doi.org/10.11648/j.ajsea.20160501.11

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ACS Style

Ting Xiang; Dazhi Pan; Haijie Pei. Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation. Am. J. Softw. Eng. Appl. 2015, 5(1), 1-6. doi: 10.11648/j.ajsea.20160501.11

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AMA Style

Ting Xiang, Dazhi Pan, Haijie Pei. Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation. Am J Softw Eng Appl. 2015;5(1):1-6. doi: 10.11648/j.ajsea.20160501.11

Copy | Download

@article{10.11648/j.ajsea.20160501.11,
  author = {Ting Xiang and Dazhi Pan and Haijie Pei},
  title = {Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation},
  journal = {American Journal of Software Engineering and Applications},
  volume = {5},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajsea.20160501.11},
  url = {https://doi.org/10.11648/j.ajsea.20160501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20160501.11},
  abstract = {In order to solve the vehicle routing problem, this paper introduces the Gauss mutation, which is based on the common particle swarm algorithm, to constitute an improved particle swarm algorithm (NPSO). In the process of solving vehicle routing problem, the NPSO is encoded by integer and proposes a new way to adjust the infeasible solutions. The particles are divided into two overlapping subgroups, and join the two-two exchange neighborhood search to iterate. Finally, the simulation experiments show that the proposed algorithm can get the optimal solution faster and better, and it has a certain validity and practicability.},
 year = {2015}
}

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TY  - JOUR
T1  - Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation
AU  - Ting Xiang
AU  - Dazhi Pan
AU  - Haijie Pei
Y1  - 2015/12/25
PY  - 2015
N1  - https://doi.org/10.11648/j.ajsea.20160501.11
DO  - 10.11648/j.ajsea.20160501.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20160501.11
AB  - In order to solve the vehicle routing problem, this paper introduces the Gauss mutation, which is based on the common particle swarm algorithm, to constitute an improved particle swarm algorithm (NPSO). In the process of solving vehicle routing problem, the NPSO is encoded by integer and proposes a new way to adjust the infeasible solutions. The particles are divided into two overlapping subgroups, and join the two-two exchange neighborhood search to iterate. Finally, the simulation experiments show that the proposed algorithm can get the optimal solution faster and better, and it has a certain validity and practicability.
VL  - 5
IS  - 1
ER  -

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