Efficient Transmission based on Genetic Evolutionary Algorithm

Jin Fan; Kit Guan Lim; Helen Sin Ee Chuo; Min Keng Tan; Ali Farzamnia; Kenneth Tze Kin Teo

doi:10.1109/IICAIET55139.2022.9936866

Efficient Transmission based on Genetic Evolutionary Algorithm

Jin Fan, Kit Guan Lim, Helen Sin Ee Chuo, Min Keng Tan, Ali Farzamnia, Kenneth Tze Kin Teo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively.

Original language	English
Title of host publication	4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781665468374
ISBN (Print)	9781665468381
DOIs	https://doi.org/10.1109/IICAIET55139.2022.9936866
Publication status	Published - 9 Nov 2022
Externally published	Yes
Event	4th IEEE International Conference on Artificial Intelligence in Engineering and Technology - Kota Kinabalu, Malaysia Duration: 13 Sep 2022 → 15 Sep 2022 Conference number: 4

Conference

Conference	4th IEEE International Conference on Artificial Intelligence in Engineering and Technology
Abbreviated title	IICAIET 2022
Country/Territory	Malaysia
City	Kota Kinabalu
Period	13/09/22 → 15/09/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IICAIET55139.2022.9936866Licence: Unspecified

Cite this

Fan, J., Lim, K. G., Ee Chuo, H. S., Tan, M. K., Farzamnia, A., & Kin Teo, K. T. (2022). Efficient Transmission based on Genetic Evolutionary Algorithm. In 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022 [9936866] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IICAIET55139.2022.9936866

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title = "Efficient Transmission based on Genetic Evolutionary Algorithm",

abstract = "Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively.",

keywords = "Evolutionary Algorithms, Genetic Algorithm, Transmission Efficiency",

author = "Jin Fan and Lim, {Kit Guan} and {Ee Chuo}, {Helen Sin} and Tan, {Min Keng} and Ali Farzamnia and {Kin Teo}, {Kenneth Tze}",

note = "Funding Information: * Corresponding authors: Kenneth Tze Kin Teo and Kit Guan Lim This work is supported by Universiti Malaysia Sabah, grant no. SLB2112 and DN20086. Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022 ; Conference date: 13-09-2022 Through 15-09-2022",

year = "2022",

month = nov,

day = "9",

doi = "10.1109/IICAIET55139.2022.9936866",

language = "English",

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booktitle = "4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Fan, J, Lim, KG, Ee Chuo, HS, Tan, MK, Farzamnia, A & Kin Teo, KT 2022, Efficient Transmission based on Genetic Evolutionary Algorithm. in 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022., 9936866, Institute of Electrical and Electronics Engineers Inc., 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, Kota Kinabalu, Malaysia, 13/09/22. https://doi.org/10.1109/IICAIET55139.2022.9936866

Efficient Transmission based on Genetic Evolutionary Algorithm. / Fan, Jin; Lim, Kit Guan; Ee Chuo, Helen Sin et al.

4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. 9936866.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient Transmission based on Genetic Evolutionary Algorithm

AU - Fan, Jin

AU - Lim, Kit Guan

AU - Ee Chuo, Helen Sin

AU - Tan, Min Keng

AU - Farzamnia, Ali

AU - Kin Teo, Kenneth Tze

N1 - Conference code: 4

PY - 2022/11/9

Y1 - 2022/11/9

N2 - Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively.

AB - Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively.

KW - Evolutionary Algorithms

KW - Genetic Algorithm

KW - Transmission Efficiency

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BT - 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

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T2 - 4th IEEE International Conference on Artificial Intelligence in Engineering and Technology

Y2 - 13 September 2022 through 15 September 2022

ER -

Efficient Transmission based on Genetic Evolutionary Algorithm

Abstract

Conference

UN SDGs

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