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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #318372
Title: Framework for computationally efficient optimal irrigation scheduling using ant colony optimization

Author
item NGUYEN, DUC CONG - University Of Adelaide
item MAIER, HOLGER - University Of Adelaide
item DANDY, GRAEME - University Of Adelaide
item Ascough Ii, James

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/9/2015
Publication Date: 12/15/2015
Citation: Nguyen, D.H., Maier, H.R., Dandy, G.C., Ascough II, J.C. 2015. Framework for computationally efficient optimal irrigation scheduling using ant colony optimization. Environmental Modelling & Software. Environmental Modelling & Software 76:37-53.

Interpretive Summary: An optimization framework is introduced with the goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework represents the problem in the form of a decision tree, including dynamic decision variable option (DDVO) adjustment during the optimization process and using ant colony optimization (ACO) as the optimization engine. A case study from the literature is presented for optimizing an irrigation schedule for seven crops. The results indicate that the proposed ACO-DDVO approach is able to find better solutions than those previously identified using linear programming. ACO-DDVO consistently outperforms an ACO algorithm using static decision variable options and penalty functions in terms of solution quality and computational efficiency. The substantial reduction in computational effort achieved by ACO-DDVO should be a major advantage in the optimization of real-world problems.

Technical Abstract: A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework achieves this goal by representing the problem in the form of a decision tree, including dynamic decision variable option (DDVO) adjustment during the optimization process and using ant colony optimization (ACO) as the optimization engine. A case study from the literature is considered for optimizing an irrigation schedule for seven crops. The results indicate that the proposed ACO-DDVO approach is able to find better solutions than those previously identified using linear programming. Furthermore, ACO-DDVO consistently outperforms an ACO algorithm using static decision variable options and penalty functions in terms of solution quality and computational efficiency. The considerable reduction in computational effort achieved by ACO-DDVO should be a major advantage in the optimization of real-world problems using complex crop simulation models.