Improved Ant Colony Optimization for Optimal Crop and Irrigation Water Allocation by Incorporating Domain Knowledge
This article has been corrected.
VIEW CORRECTIONThis article is a reply.
VIEW THE ORIGINAL ARTICLEPublication: Journal of Water Resources Planning and Management
Volume 142, Issue 9
Abstract
An improved ant colony optimization (ACO) formulation for the allocation of crops and water to different irrigation areas is developed. The formulation enables dynamic decision variable option (DDVO) adjustment and makes use of domain knowledge through visibility factors (VFs) to bias the search towards selecting crops that maximize net returns and water allocations that result in the largest net return for the selected crop, given a fixed total volume of water. The performance of this formulation is compared with that of other ACO algorithm variants (without and with domain knowledge) for two case studies, including one from the literature and one introduced in this paper for different water-availability scenarios within an irrigation district located in Loxton, South Australia near the River Murray. The results for both case studies indicate that the use of VFs (1) increases the ability to identify better solutions at all stages of the search; and (2) reduces the computational time to identify near-optimal solutions. Furthermore, the savings in computational time obtained by using VFs and DDVO adjustment should be considerable for ACO application to problems such as detailed irrigation scheduling that rely on more-complex crop models than those used in the case studies presented in the paper.
Get full access to this article
View all available purchase options and get full access to this article.
References
Afshar, A., Massoumi, F., Afshar, A., and Mariño, M. (2015). “State of the art review of ant colony optimization applications in water resource management.” Water Resour. Manage., 29(11), 3891–3900.
Afshar, M. H. (2007). “Partially constrained ant colony optimization algorithm for the solution of constrained optimization problems: Application to storm water network design.” Adv. Water Resour., 30(4), 954–965.
Afshar, M. H. (2010). “A parameter free continuous ant colony optimization algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach.” Adv. Eng. Software, 41(2), 188–195.
Afshar, M. H., and Moeini, R. (2008). “Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems.” Water Resour. Manage., 22(12), 1835–1857.
Azamathulla, H. M., Wu, F.-C., Ghani, A. A., Narulkar, S. M., Zakaria, N. A., and Chang, C. K. (2008). “Comparison between genetic algorithm and linear programming approach for real time operation.” J. Hydro-Environ. Res., 2(3), 172–181.
Bi, W., Dandy, G. C., and Maier, H. R (2016). “Use of domain knowledge to increase the convergence rate of evolutionary algorithms for optimizing the cost and resilience of water distribution systems.” J. Water Resour. Plann. Manage., in press.
Bi, W., Dandy, G. C., and Maier, H. R. (2015). “Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge.” Environ. Modell. Software, 69, 370–381.
Bonissone, P. P., Subbu, R., Eklund, N., and Kiehl, T. R. (2006). “Evolutionary algorithms + domain knowledge = real-world evolutionary computation.” Evol. Comput. IEEE Trans. on, 10(3), 256–280.
Creaco, E., and Franchini, M. (2012). “Fast network multi-objective design algorithm combined with an a posteriori procedure for reliability evaluation under various operational scenarios.” Urban Water J., 9(6), 385–399.
Dorigo, M., and Di Caro, G. (1999). “Ant colony optimization: A new meta-heuristiced.” Proc., 1999 Congress on Evolutionary Computation, IEEE, New York.
Dorigo, M., Maniezzo, V., and Colorni, A. (1996). “Ant system: Optimization by a colony of cooperating agents.” IEEE Trans. Syst. Man Cybern. Part B: Cybern., 26(1), 29–41.
Fallah-Mehdipour, E., Bozorg Haddad, O., and Mariño, M. (2013). “Extraction of multicrop planning rules in a reservoir system: Application of evolutionary algorithms.” J. Irrig. Drain. Eng., 490–498.
Foong, W. K., Maier, H. R., and Simpson, A. R. (2008a). “Power plant maintenance scheduling using ant colony optimization: An improved formulation.” Eng. Optimiz., 40(4), 309–329.
Foong, W. K., Simpson, A. R., Maier, H. R., and Stolp, S. (2008b). “Ant colony optimization for power plant maintenance scheduling optimization—A five-station hydropower system.” Ann. Oper. Res., 159(1), 433–450.
Han, Y., Huang, Y. F., and Wang, G. Q. (2011). “Interval-parameter linear optimization model with stochastic vertices for land and water resources allocation under dual uncertainty.” Environ. Eng. Sci., 28(3), 197–205.
Kadu, M., Gupta, R., and Bhave, P. (2008). “Optimal design of water networks using a modified genetic algorithm with reduction in search space.” J. Water Resour. Plann. Manage., 147–160.
Kang, D., and Lansey, K. (2012). “Revisiting optimal water-distribution system design: Issues and a heuristic hierarchical approach.” J. Water Resour. Plann. Manage., 208–217.
Keedwell, E., and Khu, S.-T. (2006). “Novel cellular automata approach to optimal water distribution network design.” J. Comput. Civ. Eng., 49–56.
King, D., Connor, J., Laughlin, I., and Meyer, W. (2012). “Interactive land use strategic assessment (ILSA) tool: Scientific methods and tool design.” Dept. of Agriculture, Fisheries and Forestry FarmReady, CSIRO, Water for a Healthy Country National Research Flagship.
Kumar, D. N., Raju, K., and Ashok, B. (2006). “Optimal reservoir operation for irrigation of multiple crops using genetic algorithms.” J. Irrig. Drain. Eng., 123–129.
Kumar, R., and Khepar, S. D. (1980). “Decision models for optimal cropping patterns in irrigations based on crop water production functions.” Agric. Water Manage., 3(1), 65–76.
López-Ibáñez, M., Prasad, T., and Paechter, B. (2008). “Ant colony optimization for optimal control of pumps in water distribution networks.” J. Water Resour. Plann. Manage., 337–346.
Maier, H. R., et al. (2003). “Ant colony optimization for design of water distribution systems.” J. Water Resour. Plann. Manage., 200–209.
Maier, H. R., et al. (2014). “Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions.” Environ. Modell. Software, 62, 271–299.
Maier, H. R., Kapelan, Z., Kasprzyk, J., and Matott, L. S. (2015). “Thematic issue on evolutionary algorithms in water resources.” Environ. Modell. Software, 69(C), 222–225.
Moeini, R., and Afshar, M. (2013). “Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems.” J. Hydroinform., 15(1), 155–173.
Moeini, R., and Afshar, M. H. (2011). “Arc-based constrained ant colony optimisation algorithms for the optimal solution of hydropower reservoir operation problems.” Can. J. Civ. Eng., 38(7), 811–824.
Nguyen, D. C. H. (2015). “Crop and water allocation.” 〈https://github.com/hiepnguyendc/Crop_Water_Allocation〉 (Dec. 8, 2015).
Nguyen, D. C. H., Maier, H. R., Dandy, G. C., and Ascough II, J. C. (2016). “Framework for computationally efficient optimal crop and water allocation using ant colony optimization.” Environ. Modell. Software, 76, 37–53.
Nicklow, J., et al. (2010). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage., 412–432.
Nixon, J., Dandy, G., and Simpson, A. (2001). “A genetic algorithm for optimizing off-farm irrigation scheduling.” J. Hydroinform., 3, 11–22.
Noory, H., Liaghat, A., Parsinejad, M., and Haddad, O. (2012). “Optimizing irrigation water allocation and multicrop planning using discrete PSO algorithm.” J. Irrig. Drain. Eng., 437–444.
Ortega Álvarez, J., De Juan Valero, J., Martín-Benito, J., and López Mata, E. (2004). “MOPECO: An economic optimization model for irrigation water management.” Irrig. Sci., 23(2), 61–75.
Reddy, M. J., and Kumar, D. N. (2007). “Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization.” Hydrol. Sci. J., 52(4), 686–701.
Singh, A. (2012). “An overview of the optimization modelling applications.” J. Hydrol., 466–467, 167–182.
Singh, A. (2014). “Irrigation planning and management through optimization modelling.” Water Resour. Manage., 28(1), 1–14.
Soundharajan, B., and Sudheer, K. P. (2009). “Deficit irrigation management for rice using crop growth simulation model in an optimization framework.” Paddy Water Environ., 7(2), 135–149.
Stützle, T., and Hoos, H. H. (2000). “MAX-MIN ant system.” Future Gener. Comput. Syst., 16(8), 889–914.
Szemis, J. M., Maier, H. R., and Dandy, G. C. (2012). “A framework for using ant colony optimization to schedule environmental flow management alternatives for rivers, wetlands, and floodplains.” Water Resour. Res., 48(8), W08502.
Szemis, J. M., Maier, H. R., and Dandy, G. C. (2014). “An adaptive ant colony optimization framework for scheduling environmental flow management alternatives under varied environmental water availability conditions.” Water Resour. Res., 50(10), 7606–7625.
Zecchin, A., Maier, H., Simpson, A., Leonard, M., and Nixon, J. (2007). “Ant colony optimization applied to water distribution system design: Comparative study of five algorithms.” J. Water Resour. Plann. Manage., 87–92.
Zecchin, A. C., Simpson, A. R., Maier, H. R., Leonard, M., Roberts, A. J., and Berrisford, M. J. (2006). “Application of two ant colony optimisation algorithms to water distribution system optimisation.” Math. Comput. Modell., 44(5–6), 451–468.
Zecchin, A. C., Simpson, A. R., Maier, H. R., Marchi, A., and Nixon, J. B. (2012). “Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem.” Water Resour. Res., 48(9), W09505.
Zecchin, A. C., Simpson, A. R., Maier, H. R., and Nixon, J. B. (2005). “Parametric study for an ant algorithm applied to water distribution system optimization.” IEEE Trans. Evol. Comput., 9(2), 175–191.
Zheng, F., Simpson, A., and Zecchin, A. (2011). “Dynamically expanding choice-table approach to genetic algorithm optimization of water distribution systems.” J. Water Resour. Plann. Manage., 547–551.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 142 • Issue 9 • September 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Sep 29, 2015
Accepted: Jan 25, 2016
Published online: Apr 22, 2016
Published in print: Sep 1, 2016
Discussion open until: Sep 22, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.