Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm

Xianhao Deng; Qiancheng Tan; Hao Liu; Yubiao Long; Yonghui Qin

doi:doi:10.11648/j.mcs.20240904.12

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Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm

Xianhao Deng

, Qiancheng Tan

, Hao Liu

, Yubiao Long

, Yonghui Qin^*

Published in Mathematics and Computer Science (Volume 9, Issue 4)

Received: 29 August 2024 Accepted: 18 September 2024 Published: 29 September 2024

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Abstract

This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.

Published in	Mathematics and Computer Science (Volume 9, Issue 4)
DOI	10.11648/j.mcs.20240904.12
Page(s)	74-87
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), 2024. Published by Science Publishing Group

Keywords

Intelligent Street Lights, Centerline, Sequence Quadratic Programming Algorithm, Genetic Algorithm

References

[1]	Yu C, Liu Z. Distributed Intelligent City Street Lamp Monitoring and Control System Based on Wireless Communication chip nRF401 [C]// International Conference on Networks Security. IEEE, 2009. https://doi.org/10.1109/NSWCTC.2009.69
[2]	Liu Y, Zheng K, Liu Y, et al. Intelligent design of street lamp based on Arduino [J]. IOP Conference Series Earth and Environmental Science, 2020, 546: 052055. https://doi.org/10.1088/1755-1315/546/5/052055
[3]	Goldberg D E. Genetic Algorithm in Search, Optimization, and Machine Learning [M]. Addison-Wesley Pub. Co, 1989. ISBN 10: 0201157675.
[4]	Vitali D, Garbuglia F, D’Alessandro V, et al. The renewable energy in led standalone streetlight [J]. International Journal of Energy Production and Management, 2017: 167-172. https://doi.org/10.2495/EQ-V2-N1-118-129
[5]	Yoon Y, Kim J. Design Hybrid Type Streetlight for Railway Station with Renewable Energy [J]. The transactions Korean Institute of Electrical Engineers, 2016, 65(12): 80-92. https://doi.org/10.5370/KIEE.2016.65.12.2103
[6]	Wright A, Vose M, Jong K, et al. Foundations of Genetic Algorithms [M]. Springer-Berlin Heidelberg; Springer, Berlin, Heidelberg: 2005. https://doi.org/10.1007/b138412
[7]	Grefenstette J. Genetic Algorithms and their Applications [M]. Taylor and Francis: 2013. ISBN: 9781134989737.
[8]	Gill P E, Murray W, Saunders M A. SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization [J]. Society for Industrial and Applied Mathematics, 2002. https://doi.org/10.1137/S0036144504446096
[9]	Dennis B H, Dulikravich G S, Han Z X. Optimization of Turbomachinery Airfoils with a Genetic/ Sequential-Quadratic-Programming Algorithm [J]. Journal of Propulsion and Power, 2001. https://doi.org/10.2514/2.5853
[10]	Aditya Shastry K., H. A. Sanjay. A modified genetic algorithm and weighted principal component analysis based feature selection and extraction strategy in agriculture [J]. Knowledge-Based Systems, 2021, 232. https://doi.org/10.1016/j.knosys.2021.107460
[11]	Wang Hao, Chen Shunhuai, Salerno Nunzio. An Approach to Ship Deck Arrangement Optimization Problem Using an Improved Multiobjective Hybrid Genetic Algorithm [J]. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/8784923
[12]	Aziza H, Krichen S. A hybrid genetic algorithm for scientific workflow scheduling in cloud environment [J]. Neural Computing and Applications, 2020, 32(18): 15263-15278. https://doi.org/10.1007/s00521-020-04878-8
[13]	Isatou Hydara, Abu Bakar Md Sultan, Hazura Zulzalil, Novia Admodisastro. Cross-Site Scripting Detection Based on an Enhanced Genetic Algorithm [J]. Indian Journal of Science and Technology, 2015, 8(30). https://doi.org/10.17485/ijst/2015/v8i30/86055
[14]	Lyantsev O D, Breikin T V, Kulikov G G, et al. OnLine Performance Optimisation of Aero-Engine Control System [J]. Automatica, 2003, 39(12): 2115-2121. https://doi.org/10.1016/S0005-1098(03)00224-3
[15]	Mansoornejad B, Mostoufi N, Jalali-Farahani F. A hybrid GA–SQP optimization technique for determination of kinetic parameters of hydrogenation reactions [J]. Computers & Chemical Engineering, 2008, 32(7): 1447-1455. https://doi.org/10.1016/j.compchemeng.2007.06.018
[16]	Balasubramanian P, Bettina S J, Pushpavanam S, et al. Kinetic Parameter Estimation in Hydrocracking Using a Combination of Genetic Algorithm and Sequential Quadratic Programming [J]. Industrial & Engineering Chemistry Research, 2003, 42(20): 4723-4731. https://doi.org/10.1021/ie021057s
[17]	Xie L, Chen Y, Chang R. Scheduling optimization of prefabricated construction projects by genetic algorithm [J]. Applied Sciences, 2021, 11(12): 5531. https://doi.org/10.3390/app11125531
[18]	Beck A, Eldar Y C. Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms [J]. SIAM Journal on Optimization, 2012, 23(3): 1480-1509. https://doi.org/10.1137/120869778

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APA Style

Deng, X., Tan, Q., Liu, H., Long, Y., Qin, Y. (2024). Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm. Mathematics and Computer Science, 9(4), 74-87. https://doi.org/10.11648/j.mcs.20240904.12

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ACS Style

Deng, X.; Tan, Q.; Liu, H.; Long, Y.; Qin, Y. Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm. Math. Comput. Sci. 2024, 9(4), 74-87. doi: 10.11648/j.mcs.20240904.12

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AMA Style

Deng X, Tan Q, Liu H, Long Y, Qin Y. Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm. Math Comput Sci. 2024;9(4):74-87. doi: 10.11648/j.mcs.20240904.12

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@article{10.11648/j.mcs.20240904.12,
  author = {Xianhao Deng and Qiancheng Tan and Hao Liu and Yubiao Long and Yonghui Qin},
  title = {Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm
},
  journal = {Mathematics and Computer Science},
  volume = {9},
  number = {4},
  pages = {74-87},
  doi = {10.11648/j.mcs.20240904.12},
  url = {https://doi.org/10.11648/j.mcs.20240904.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20240904.12},
  abstract = {This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.
},
 year = {2024}
}

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TY  - JOUR
T1  - Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm

AU  - Xianhao Deng
AU  - Qiancheng Tan
AU  - Hao Liu
AU  - Yubiao Long
AU  - Yonghui Qin
Y1  - 2024/09/29
PY  - 2024
N1  - https://doi.org/10.11648/j.mcs.20240904.12
DO  - 10.11648/j.mcs.20240904.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 74
EP  - 87
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20240904.12
AB  - This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.

VL  - 9
IS  - 4
ER  -

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Author Information

Xianhao Deng

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, P. R. China

Contact Email

http://orcid.org/0009-0003-1613-349X
Qiancheng Tan

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, P. R. China

Contact Email

http://orcid.org/0009-0007-7727-8640
Hao Liu

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, P. R. China

Contact Email

http://orcid.org/0009-0009-6095-6649
Yubiao Long

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, P. R. China

Contact Email

http://orcid.org/0009-0005-8498-8558
Yonghui Qin

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, P. R. China; Center for Applied Mathematics of Guangxi (GUET), Guilin, P. R. China; Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (GUET), Guilin, P. R. China

Contact Email

http://orcid.org/0000-0003-2020-4257

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Plain Text BibTeX RIS

APA Style

Deng, X., Tan, Q., Liu, H., Long, Y., Qin, Y. (2024). Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm. Mathematics and Computer Science, 9(4), 74-87. https://doi.org/10.11648/j.mcs.20240904.12

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ACS Style

Deng, X.; Tan, Q.; Liu, H.; Long, Y.; Qin, Y. Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm. Math. Comput. Sci. 2024, 9(4), 74-87. doi: 10.11648/j.mcs.20240904.12

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AMA Style

Deng X, Tan Q, Liu H, Long Y, Qin Y. Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm. Math Comput Sci. 2024;9(4):74-87. doi: 10.11648/j.mcs.20240904.12

Copy | Download

@article{10.11648/j.mcs.20240904.12,
  author = {Xianhao Deng and Qiancheng Tan and Hao Liu and Yubiao Long and Yonghui Qin},
  title = {Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm
},
  journal = {Mathematics and Computer Science},
  volume = {9},
  number = {4},
  pages = {74-87},
  doi = {10.11648/j.mcs.20240904.12},
  url = {https://doi.org/10.11648/j.mcs.20240904.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20240904.12},
  abstract = {This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm

AU  - Xianhao Deng
AU  - Qiancheng Tan
AU  - Hao Liu
AU  - Yubiao Long
AU  - Yonghui Qin
Y1  - 2024/09/29
PY  - 2024
N1  - https://doi.org/10.11648/j.mcs.20240904.12
DO  - 10.11648/j.mcs.20240904.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 74
EP  - 87
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20240904.12
AB  - This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.

VL  - 9
IS  - 4
ER  -

Copy | Download