Ant-Colony-Algorithm-Based Intelligent Transmission Network Planning

Jingzhong      Yuan; Jia      Guo; Jinghai      Xie; Shihua      Lu; Dongyu      Su; Mi      Sun; Mohd   Asif   Shah

Abstract

Background: The efficiency of wireless sensor networks is limited by limitations in energy supply. Efficient routing strategies should be designed to save and balance the energy consumption of each node in a wireless sensor network.

Aim: In this study, a transmission network based on an ant colony algorithm was proposed to meet the power load demands of a city.

Objective: Based on the chaos ant colony algorithm, using a combination of wireless sensor network and node residual energy factors, a neighbor selection strategy was proposed.

Results: The optimal result was 1896, and additional lines were: N_14-15= 2, N_4-16= 1, N_5-12= 2, N_7-13= L, N_6-14= 1, N_7-8. The coding method of solving transmission network planning based on multi-stage and multi-dimensional control variables was employed to decompose each control variable into two variables. The sum of total weight and non-zero bits was transformed into high-dimensional variables in state transition probability.

Conclusion: The key analysis showed that the ant colony algorithm, as a simulated evolutionary algorithm, is an efficient internal heuristic method.

Keywords: Ant colony algorithm, intelligent transmission network, power load, optimization, evolutionary algorithm, wireless sensor network.

[1]
J.M. Zhu, H. Ren,  and Z. Meng, "A granular ant colony algorithm for power distribution network planning", Int. J. Hybrid Inf. Technol., vol. 9, no. 11, pp. 169-180, 2016.
 [http://dx.doi.org/10.14257/ijhit.2016.9.11.15]
[2]
J. Fang, "Clustering and path planning for wireless sensor networks based on improved ant colony algorithm", Int. J. Online Biomed. Eng., vol. 15, no. 1, p. 129, 2019.
 [http://dx.doi.org/10.3991/ijoe.v15i01.9784]
[3]
Y. Wang,  and C. Wang, "Based on the ant colony algorithm is a distributed intrusion detection method", Int. J. Secur. Appl., vol. 9, no. 4, pp. 141-152, 2015.
 [http://dx.doi.org/10.14257/ijsia.2015.9.4.14]
[4]
Y. Wang, M. Zhang,  and W. Shu, "An emerging intelligent optimization algorithm based on trust sensing model for wireless sensor networks", EURASIP J. Wirel. Commun. Netw., vol. 2018, no. 1, p. 145, 2018.
 [http://dx.doi.org/10.1186/s13638-018-1174-6]
[5]
Z. Luo, Z. Luo, Y. Zhang, Y. Miao,  and T. Ding, "An efficient intelligent algorithm based on wsns of the drug control system", Teh. Vjesn., vol. 24, no. 1, pp. 273-282, 2017.
[6]
L.A. Gallego, L.P. Garcés, M. Rahmani,  and R.A. Romero, "High-performance hybrid genetic algorithm to solve transmission network expansion planning", IET Gener. Transm. Distrib., vol. 11, no. 5, pp. 1111-1118, 2017.
 [http://dx.doi.org/10.1049/iet-gtd.2016.0511]
[7]
Li K., "& Yuan W. (2021) The nexus between industrial growth and electricity consumption in China-New evidence from a quantile-on-quantile approach", Energy, p. 231, 120991, .
 [http://dx.doi.org/10.1016/j.energy.2021.120991]
[8]
H. Wu, "Intelligent traffic scheduling algorithm based on hybrid differential evolution strategy,", Int. J. Simul. Syst.. vol. 17, no. 40, pp. 20.1-20.5, 2016.
[9]
S. Liu, Y. Xu, L. Guo, M. Shao,  and D. An, "Multi-scale personnel deep feature detection algorithm based on extended-yolov3", J. Intell. Fuzzy Syst., vol. 40, no. 7, pp. 1-14, 2020.
[10]
R. Chen, J. Li, T. Shang,  and J. Zhang, "Intelligent fault diagnosis of gearbox based on improved fireworks algorithm and probabilistic neural network. Nongye Gongcheng Xuebao", Nongye Gongcheng Xuebao (Beijing), vol. 34, no. 17, pp. 192-198, 2018.
[11]
H. Huang, D. Sha, Y. Zhang,  and P. Li, "Routing algorithm and traffic light control based on vehicular delay-tolerant networks", J. Commun., vol. 11, no. 3, pp. 305-316, 2016.
[12]
Y. Chen, X. Xu,  and Y. Wang, "Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm", Discrete Contin. Dyn. Syst., vol. 12, no. 4&5, pp. 887-900, 2019.
 [http://dx.doi.org/10.3934/dcdss.2019059]
[13]
B. Ali, G. Murtaza, T. Mahmood, H.M. Bilal,  and S. Memon, "Energy routing algorithm based on ospf protocol and virtual circuit switching mode in energy local area network", Int. J. Comput. Complex. Intell. Algorithms, vol. 1, no. 1, p. 1, 2019.
 [http://dx.doi.org/10.1504/IJCCIA.2019.10024515]
[14]
K. Sharma, R. Poonia, R. Kumar, S. Sunda,  and D.N. Le, "Map matching algorithm: Trajectory and sequential map analysis on road network",  Ind. Netw. Intell. Syst..
 vol. 5, no. 16, pp. 155999, 2018. [http://dx.doi.org/10.4108/eai.29-11-2018.155999]
[15]
M. Guo,  and M. Xiao, "Mssn: An attribute-aware transmission algorithm exploiting node similarity for opportunistic social networks", Information, vol. 10, no. 10, p. 299, 2019.
 [http://dx.doi.org/10.3390/info10100299]
[16]
H. Liang, J. Zou,  and W. Liang, "An early intelligent diagnosis model for drilling overflow based on ga-bp algorithm", Cluster Comput., vol. 22, no. 5, pp. 10649-10668, 2019.
 [http://dx.doi.org/10.1007/s10586-017-1152-5]
[17]
M. Kumar,  and A. Ch, "Obc-woa: Opposition-based chaotic whale optimization algorithm for energy efficient clustering in wireless sensor network", Int. J. Intell. Eng. Syst., vol. 12, no. 6, pp. 249-258, 2019.
 [http://dx.doi.org/10.22266/ijies2019.1231.24]
[18]
Y. Li, "High accuracy data fusion algorithm for privacy serving in wireless sensor networks", J. Intell. Fuzzy Syst., vol. 37, no. 4, pp. 1-6, 2019.
 [http://dx.doi.org/10.3233/JIFS-179297]
[19]
B. Mahdad,  and S. Kamel, "New strategy based modified salp swarm algorithm for optimal reactive power planning: A case study of the algerian electrical system (114 bus). Generation", Transm. Distrib., IET.
 vol. 13, no. 20, pp. 4523-4540, 2019. [http://dx.doi.org/10.1049/iet-gtd.2018.5772]
[20]
M. Ebbecke, "Smart data transmission for intelligent heat meters", Euroheat Power, vol. 16, no. 2, pp. 37-38, 2019.
[21]
J. Gan, X. Wang, J. Zhou, L. Tang,  and L. Yuan, "Intelligent monitoring network construction based on the utilization of the Internet of Things (IoT) in the metallurgical coking process", Open Phys., vol. 16, no. 1, pp. 656-662, 2018.
 [http://dx.doi.org/10.1515/phys-2018-0083]
[22]
K. Selvakumar, L. Sairamesh,  and A. Kannan, "An intelligent energy aware secured algorithm for routing in wireless sensor networks", Wirel. Pers. Commun., vol. 96, no. 3, pp. 4781-4798, 2017.
[23]
W. Wang,  and G. Tong, "Multi-path unequal clustering protocol based on ant colony algorithm in wireless sensor networks", IET Networks, vol. 9, no. 2, pp. 56-63, 2020.
 [http://dx.doi.org/10.1049/iet-net.2019.0096]
[24]
X. Guo,  and Y. Liu, "Intelligent traffic cloud computing system based on ant colony algorithm", J. Intell. Fuzzy Syst., vol. 39, no. 5, pp. 1-12, 2020.
 [http://dx.doi.org/10.3233/JIFS-179980]
[25]
H. Yuan,  and Y. Han, "Routing algorithm of health monitoring network in cps old building structure based on genetic ant colony algorithm", Int. J. Online Eng., vol. 12, no. 10, p. 24, 2016.
 [http://dx.doi.org/10.3991/ijoe.v12i10.6201]
[26]
T. Li, F. Ruan, Z. Fan, J. Wang,  and J.U. Kim, "An improved pegasis routing protocol based on neural network and ant colony algorithm", Int. J. Future Gener. Commun. Netw., vol. 8, no. 6, pp. 149-160, 2015.
 [http://dx.doi.org/10.14257/ijfgcn.2015.8.6.15]
[27]
G. Veselov, A. Tselykh, A. Sharma,  and R. Huang, "Applications of artificial intelligence in evolution of smart cities and societies", Informaticavol. 2021, 45, no. 5, pp. 1-2, .
[28]
H. Zhang, Z. Li, W. Shu,  and J. Chou, "Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks", EURASIP J. Wirel. Commun. Netw., vol. 2019, no. 1, p. 152, 2019.
 [http://dx.doi.org/10.1186/s13638-019-1472-7]

Cite As

Recent Advances in Electrical & Electronic Engineering

Ant-Colony-Algorithm-Based Intelligent Transmission Network Planning

Abstract