Ant Colony Optimization Approach for Optimizing Irrigation System Layout: Case of Gravity and Collective Network

  • Sahar MarouaneEmail author
  • Fahad Alahmari
  • Jalel Akaichi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)


Irrigation is the artifcial employment of water to the plants which is used to assist in the growing of agricultural crops. There are several methods of irrigation that differ in how the water is distributed between fields. In fact irrigation systems can be classified into two main categories: gravity irrigation and pressurized irrigation. The allocation of water to the fields is done either collectively or individually. Whatever the used irrigation technique, the goal is to have a well-designed irrigation system. This research applies the metaheuristic method of ant colony optimization (ACO) to design an optimal irrigation layout. The proposed approach uses ACO rules to generate the possible links between fields which distribute water to farmers. And the algorithm ant system was applied to find the optimal link.


Ant colony optimization Irrigation system layout Gravity irrigation Collective irrigation 


  1. 1.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press Cambridge, London (2004)zbMATHGoogle Scholar
  2. 2.
    Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M.: The self-organizing exploratory pattern of the argentine ant. J. lnsect Behav. 3(2), 159–168 (1990)CrossRefGoogle Scholar
  3. 3.
    Dorigo, M.: Optimization, learning and natural algorithms (in Italian). Ph.D. Thesis, Department of Electronics and Polytechnic of Milan, Italy (1992)Google Scholar
  4. 4.
    Bullnheimer, B., Strauss, C.: A new rank based version of the ant system-A computational study. In: Adaptive Information Systems and Modelling in Economics and Management Science (1997)Google Scholar
  5. 5.
    Stützle, T., Hoos, H.: Max-Min ant system. Future Gener. Comput. Syst. 16(9), 889–914 (2000)CrossRefzbMATHGoogle Scholar
  6. 6.
    Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Gambardella, L.: Ant-Q: A reinforcement learning approach to the traveling salesman problem (1997)Google Scholar
  8. 8.
    Gambardella, L., Dorigo, M.: Has-sop: Hybrid ant system for the sequential ordering problem. Technical Report IDSIA 11–97 (2000)Google Scholar
  9. 9.
    Maniezzo, V., Colorni, A.: The ant system applied to the quadratic assignment problem. IEEE Trans. Knowl. Data Eng. 11(5), 769–778 (1999)CrossRefGoogle Scholar
  10. 10.
    Solnon, C.: Combining two pheromone structures for solving the car sequencing problem with ant colony optimization (2008)Google Scholar
  11. 11.
    Fenet, S., Solnon, C.: Searching for maximum cliques with ant colony optimization (2003)Google Scholar
  12. 12.
    Zapata, N., Playan, E., Lecina, S.: From on-farm solid-set sprinkler irrigation design to collective irrigation network design in windy areas. Agric. Water Manag. 87(2), 187–199 (2007)CrossRefGoogle Scholar
  13. 13.
    González, P.M., Poyato, C., Díaz, R.: Optimization of irrigation scheduling using soil water balance and genetic algorithms. Water Resour. Manage. 30(8), 2815–2830 (2016)CrossRefGoogle Scholar
  14. 14.
    Carríon, F., Sanchez-Vizcaino, J., Moreno, M.: Optimization of groundwater abstraction system and distribution pipe in pressurized irrigation systems for minimum cost. Irrig. Sci. 34(2), 145–159 (2016)CrossRefGoogle Scholar
  15. 15.
    García, F., Montesinos, P., Díaz, J.: Energy cost optimization in pressurized irrigation networks. Irrig. Sci. 34(1), 1–13 (2015)CrossRefGoogle Scholar
  16. 16.
    Sonit, A., Hemlata, K.: Optimization of water use in summer rice through drip irrigation. J. Soil Water Conserv. 14(2), 157–159 (2015)Google Scholar
  17. 17.
    Izquiel, A., Carriíon, P., Moreno, M.A.: Optimal reservoir capacity for centre pivot irrigation water supply Maize cultivation in Spain. Biosyst. Eng. 135, 61–72 (2015)CrossRefGoogle Scholar
  18. 18.
    Mariano, C.E., Morales, E.: A multiple objective ant-Q algorithms for the design of water distribution irrigation network (1999)Google Scholar
  19. 19.
    Tu, Q., Li, H., Wang, X., Chen, C.: Ant colony optimization for the design of small scale irrigation systems. Water Resour. Manage. 29(7), 2323–2339 (2015)CrossRefGoogle Scholar
  20. 20.
    Duc, C.H.N., Holger, R.M., Graeme, C.D., James, C.A.: Framework for computationally efficient optimal crop and water allocation using ant colony optimization. Environ. Model. Softw. 76, 37–53 (2016). ElsevierCrossRefGoogle Scholar
  21. 21.
    Kumar, D.N., Reddy, M.J.: Ant colony optimization for multi-purpose reservoir operation. Water Resour. Manage. 20, 879–898 (2006). ElsevierCrossRefGoogle Scholar
  22. 22.
    Nguyen, T.D., Do, P.T.: An ant colony optimization algorithm for solving group steiner problem. In: IEEE Fifth International Conference Communications and Electronics (ICCE), pp. 244–249 (2014)Google Scholar
  23. 23.
    Dorigo, M., Maniezzo, V., Colorni, A.: An investigation of some properties of an ant algorithm. In: Appeard in Proceeding of the Parallel Problem Solving from Nature Conference, Brussels, Belguim. Elsevier (1992)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.High Institute of ManagementUniversity of TunisTunisTunisia
  2. 2.College of Computer ScienceKing Khaled University AbhaAbhaSaudi Arabia

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