A novel greedy adaptive ant colony algorithm for shortest path of irrigation groups

Chenyang Zhan; Min Tian; Yang Liu; Jie Zhou; Xiang Yi; Chenyang Zhan; Min Tian; Yang Liu; Jie Zhou; Xiang Yi

doi:10.3934/mbe.2022419

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 9018-9038. doi: 10.3934/mbe.2022419

Previous Article Next Article

Research article Special Issues

A novel greedy adaptive ant colony algorithm for shortest path of irrigation groups

1.
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China
2.
College of Information Science and Technology, Shihezi University, Shihezi, 832000, China
3.
The Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Group, Shihezi University, Shihezi, 832003, China

Academic Editor: Runzhang Xu

Received: 04 April 2022 Revised: 06 June 2022 Accepted: 13 June 2022 Published: 22 June 2022

With the full-scale implementation of facility agriculture, the laying of a water distribution network (WDN) on farmland plays an important role in irrigating crops. Especially in large areas of farmland, with the parameters of moisture sensors, the staff can divide the WDN into several irrigation groups according to the soil moisture conditions in each area and irrigate them in turn, so that irrigation can be carried out quickly and efficiently while meeting the demand for irrigation. However, the efficiency of irrigation is directly related to the pipe length of each irrigation group of the WDN. Obtaining the shortest total length of irrigation groups is a path optimization problem. In this paper, a grouped irrigation path model is designed, and a new greedy adaptive ant colony algorithm (GAACO) is proposed to shorten the total length of irrigation groups. To verify the effectiveness of GAACO, we compare it with simple modified particle swarm optimization (SMPSO), chaos-directed genetic algorithms (CDGA) and self-adaptive ant colony optimization (SACO), which are currently applied to the path problem. The simulation results show that GAACO can effectively shorten the total path of the irrigation group for all cases from 30 to 100 water-demanding nodes and has the fastest convergence speed compared to SMPSO, CDGA and SACO. As a result, GAACO can be applied to the shortest pipeline path problem for irrigation of farmland groups.
- irrigation pipe network,
- shortest path,
- path model,
- greedy adaptive ant colony algorithm
Citation: Chenyang Zhan, Min Tian, Yang Liu, Jie Zhou, Xiang Yi. A novel greedy adaptive ant colony algorithm for shortest path of irrigation groups[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9018-9038. doi: 10.3934/mbe.2022419

Related Papers:

Abstract

With the full-scale implementation of facility agriculture, the laying of a water distribution network (WDN) on farmland plays an important role in irrigating crops. Especially in large areas of farmland, with the parameters of moisture sensors, the staff can divide the WDN into several irrigation groups according to the soil moisture conditions in each area and irrigate them in turn, so that irrigation can be carried out quickly and efficiently while meeting the demand for irrigation. However, the efficiency of irrigation is directly related to the pipe length of each irrigation group of the WDN. Obtaining the shortest total length of irrigation groups is a path optimization problem. In this paper, a grouped irrigation path model is designed, and a new greedy adaptive ant colony algorithm (GAACO) is proposed to shorten the total length of irrigation groups. To verify the effectiveness of GAACO, we compare it with simple modified particle swarm optimization (SMPSO), chaos-directed genetic algorithms (CDGA) and self-adaptive ant colony optimization (SACO), which are currently applied to the path problem. The simulation results show that GAACO can effectively shorten the total path of the irrigation group for all cases from 30 to 100 water-demanding nodes and has the fastest convergence speed compared to SMPSO, CDGA and SACO. As a result, GAACO can be applied to the shortest pipeline path problem for irrigation of farmland groups.

References

[1]	J. Zhang, H. Jing, K. Dong, Z. Jin, J. Ma, The effect of drip irrigation under mulch on groundwater infiltration and recharge in a semi-arid agricultural region in China, Water Supply, 2022. https://doi.org/10.2166/ws.2022.033 doi: 10.2166/ws.2022.033
[2]	C. Schwaller, Y. Keller, B. Helmreich, J. E. Drewes, Estimating the agricultural irrigation demand for planning of non-potable water reuse projects, Agric. Water Manage., 244 (2021). https://doi.org/10.1016/j.agwat.2020.106529 doi: 10.1016/j.agwat.2020.106529
[3]	L. Garcia, L. Parra, J. M. Jimenez, J. Lloret, P. Lorenz, IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture, Sensors (Basel), 20 (2020). https://doi.org/10.3390/s20041042 doi: 10.3390/s20041042
[4]	B. M. Pant, V. Snasel, Design optimization of water distribution networks through a novel differential evolution, Ieee Access, 9 (2021), 16133–16151. https://doi.org/10.1109/access.2021.3052032 doi: 10.1109/access.2021.3052032
[5]	S. Khalifeh, S. Akbarifard, V. Khalifeh, E. Zallaghi, Optimization of water distribution of network systems using the Harris Hawks optimization algorithm (Case study: Homashahr city), MethodsX, 7 (2020), 100948–100948. https://doi.org/10.1016/j.mex.2020.100948 doi: 10.1016/j.mex.2020.100948
[6]	F. Zeng, X. Li, K. Li, Modeling complexity in engineered infrastructure system: Water distribution network as an example, Chaos, 27 (2017). https://doi.org/10.1063/1.4975762 doi: 10.1063/1.4975762
[7]	G. Angella, M. G. Vila, J. M. Lopez, G. Barraza, R. Salgado, S. P. Angueira, et al., Quantifying yield and water productivity gaps in an irrigation district under rotational delivery schedule, Irrig. Sci., 34 (2016), 71–83. https://doi.org/10.1007/s00271-015-0486-0 doi: 10.1007/s00271-015-0486-0
[8]	M. F. Moreno-Perez, J. Roldan-Canas, Assessment of irrigation water management in the Genil-Cabra (Cordoba, Spain) irrigation district using irrigation indicators, Agric. Water Manage., 120 (2013), 98–106. https://doi.org/10.1016/j.agwat.2012.06.020 doi: 10.1016/j.agwat.2012.06.020
[9]	O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, M. H. D. N. Hindia, An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges, Ieee Int. Things J., 5 (2018), 3758–3773. https://doi.org/10.1109/jiot.2018.2844296 doi: 10.1109/jiot.2018.2844296
[10]	O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, X. Wang, Internet of Things for the future of smart agriculture: A comprehensive survey of emerging technologies, Ieee-Caa J. Automat. Sin., 8 (2021), 718–752. https://doi.org/10.1109/jas.2021.1003925 doi: 10.1109/jas.2021.1003925
[11]	A. Tzounis, N. Katsoulas, T. Bartzanas, C. Kittas, Internet of Things in agriculture, recent advances and future challenges, Biosyst. Eng., 164 (2017), 31–48. https://doi.org/10.1016/j.biosystemseng.2017.09.007 doi: 10.1016/j.biosystemseng.2017.09.007
[12]	Y. N. Chai, Y. M. Zeng, Adaptation to quantitative regulation of agricultural water resources: Mosaic cropping pattern and rotational irrigation in China, Water Altern. Interdiscip. J. Water Polit. Develop., 14 (2021), 395–412. Available from: https://www.webofscience.com/wos/alldb/full-record/WOS:000579072200016.
[13]	A. Fouial, I. F. Garcia, C. Bragalli, A. Brath, N. Lamaddalena, J. A. R. Diaz, Optimal operation of pressurised irrigation distribution systems operating by gravity, Agric. Water Manage., 184 (2017), 77–85. https://doi.org/10.1016/j.agwat.2017.01.010 doi: 10.1016/j.agwat.2017.01.010
[14]	S. Buhan, D. Kucuk, M. S. Cinar, U. Guvengir, T. Demirci, Y. Yilmaz, et al., A scalable river flow forecast and basin optimization system for hydropower plants, Ieee Trans. Sustainable Energy, 11 (2020), 2220–2229. https://doi.org/10.1109/tste.2019.2952450. doi: 10.1109/tste.2019.2952450
[15]	J. A. Ruiz-Vanoye, R. Barrera-Camara, O. Diaz-Parra, A. Fuentes-Penna, J. Perez Ortega, B. Bernabe Loranca, et al., Surveying the optimization problems of water distribution networks, Polish J. Environ. Stud., 27 (2018), 1425–1432. https://doi.org/10.15244/pjoes/76502 doi: 10.15244/pjoes/76502
[16]	N. Elshaboury, T. Attia, M. Marzouk, Application of evolutionary optimization algorithms for rehabilitation of water distribution networks, J. Construct. Eng. Manage., 146 (2020). https://dx.doi.org/10.1061/(asce)co.1943-7862.0001856 doi: 10.1061/(asce)co.1943-7862.0001856
[17]	T. T. Tanyimboh, Redundant binary codes in genetic algorithms: multi-objective design optimization of water distribution networks, Water Supply, 21 (2021), 444–457. https://doi.org/10.2166/ws.2020.329 doi: 10.2166/ws.2020.329
[18]	H. A. El-Ghandour, E. Elbeltagi, Comparison of five evolutionary algorithms for optimization of water distribution networks, J. Comput. Civil Eng., 32 (2018). https://doi.org/10.1061/(asce)cp.1943-5487.0000717 doi: 10.1061/(asce)cp.1943-5487.0000717
[19]	Z. Wu, Optimization of distribution route selection based on particle swarm algorithm, Int. J. Simul. Modell., 13 (2014), 230–242. https://doi.org/10.2507/ijsimm13(2)co9 doi: 10.2507/ijsimm13(2)co9
[20]	C. Li, Y. Liu, J. Xiao, J. Zhou, MCEAACO-QSRP: A novel QoS secure routing protocol for industrial Internet of Things, IEEE Int. Things J., (2022), 1–1. https://doi.org/10.1109/JIOT.2022.3162106. doi: 10.1109/JIOT.2022.3162106
[21]	Y. Liu, C. Li, Y. Zhang, M. Xu, J. Xiao, J. Zhou, HPCP-QCWOA: High performance clustering protocol based on quantum clone whale optimization algorithm in integrated energy system, Future Gener. Comput. Syst., 135 (2022), 315–332. https://doi.org/10.1016/j.future.2022.05.001 doi: 10.1016/j.future.2022.05.001
[22]	A. Moghaddam, A. Alizadeh, A. Faridhosseini, A. N. Ziaei, D. F. Heravi, Optimal design of water distribution networks using simple modified particle swarm optimization approach, Desalin. Water Treat., 104 (2018), 99–110. https://doi.org/10.5004/dwt.2018.21911 doi: 10.5004/dwt.2018.21911
[23]	S. N. Poojitha, V. Jothiprakash, B. Sivakumar, Chaos-directed genetic algorithms for water distribution network design: an enhanced search method, Stochastic Environ. Res. Risk Assess., 2022. https://doi.org/10.1007/s00477-022-02200-7 doi: 10.1007/s00477-022-02200-7
[24]	S. Bahoosh, R. Bahoosh, A. Haghighi, Development of a self-adaptive ant colony optimization for designing pipe networks, Water Resour. Manage., 33 (2019), 4715–4729. https://doi.org/10.1007/s11269-019-02379-5 doi: 10.1007/s11269-019-02379-5
[25]	A. A. Coco, J. C. Abreu, T. F. Noronha, A. C. Santos, An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem, J. Global Optim., 60 (2014), 265–287. https://doi.org/10.1007/s10898-017-0511-3 doi: 10.1007/s10898-017-0511-3
[26]	L. Lozano, D. Duque, A. L. Medaglia, A. L. Medaglia, An exact algorithm for the elementary shortest path problem with resource constraints, Transp. Sci., 50 (2016), 348–357. https://doi.org/10.1002/net.20033 doi: 10.1002/net.20033
[27]	C. R. Suribabuuribabu, Resilience-based optimal design of water distribution network, Appl. Water Sci., 7 (2017), 4055–4066. https://doi.org/10.1007/s13201-017-0560-2 doi: 10.1007/s13201-017-0560-2
[28]	M. E. Ali, Knowledge-based optimization model for control valve locations in water distribution networks, J. Water Resour. Plann. Manage., 141 (2015). https://doi.org/10.1061/(asce)wr.1943-5452.0000438 doi: 10.1061/(asce)wr.1943-5452.0000438
[29]	J. Cota-Ruiz, P. Rivas-Perea, E. Sifuentes, R. Gonzalez-Landaeta, A recursive shortest path routing algorithm with application for wireless sensor network localization, Ieee Sens. J., 16 (2016), 463–4637. https://doi.org/10.1109/jsen.2016.2543680 doi: 10.1109/jsen.2016.2543680
[30]	M. R. Torkomany, H. S. Hassan, A. Shoukry, A. M. Abdelrazek, M. Elkholy, An enhanced multi-objective particle swarm optimization in water distribution systems design, Water, 13 (2021). https://doi.org/10.3390/w13101334 doi: 10.3390/w13101334
[31]	H. Fallah, O. Kisi, S. Kim, M. Rezaie-Balf, A new optimization approach for the least-cost design of water distribution networks: Improved crow search algorithm, Water Resour. Manage., 33 (2019), 3595–3613. https://doi.org/10.1007/s11269-019-02322-8 doi: 10.1007/s11269-019-02322-8
[32]	R. M. Ezzeldin, B. Djebedjian, Optimal design of water distribution networks using whale optimization algorithm, Urban Water J., 17 (2020), 14–22. https://doi.org/10.1080/1573062x.2020.1734635 doi: 10.1080/1573062x.2020.1734635
[33]	W. W. Xia, C. Di, H. N. Guo, S. H. Li, Reinforcement learning based stochastic shortest path finding in wireless sensor networks, Ieee Access, 7 (2019), 157807–157817. https://doi.org/10.1109/access.2019.2950055 doi: 10.1109/access.2019.2950055
[34]	Z. G. Cao, H. L. Guo, J. Zhang, D. Niyato, U. Fastenrath, Finding the shortest path in stochastic vehicle routing: A cardinality minimization approach, Ieee Trans. Intell. Transp. Syst., 17 (2016), 1688–1702. https://doi.org/10.1109/tits.2015.2498160 doi: 10.1109/tits.2015.2498160
[35]	J. K. Wang, W. Z. Chi, C. M. Li, C. Q. Wang, M. Q. H. Meng, Neural RRT: Learning-based optimal path planning, Ieee Trans. Automat. Sci. Eng.*, 17 (2020), 1748–1758. https://doi.org/10.1109/tase.2020.2976560 doi: 10.1109/tase.2020.2976560
[36]	Q. Y. Tao, H. Y. Sang, H. W. Guo, P. Wang, Improved particle swarm optimization algorithm for AGV path planning, Ieee Access, 9 (2021), 33522–33531. https://doi.org/10.1109/access.2021.3061288 doi: 10.1109/access.2021.3061288

Reader Comments

Your name:*

Email:*
© 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)