Framework for computationally efficient optimal crop and water allocation using ant colony optimization. (February 2016)

• Record Type: Journal Article
• Authors: Nguyen, Duc Cong Hiep; Maier, Holger R.; Dandy, Graeme C.; Ascough, James C.
• Is Part Of: Environmental modelling & software. Volume 76(2016:Feb.)
• Page Start: 37
• Page End: 53
• Publication Date: 2016-02
• Subjects: Optimization -- Irrigation -- Water allocation -- Cropping patterns -- Ant colony optimization -- Search space; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.11.003
• Languages: English
• ISSNs: 1364-8152

Abstract

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 to optimal crop and water allocation. 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 literature is considered to evaluate the utility of the framework. 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. Highlights: New computationally efficient optimization framework for crop and water allocation. Use of domain knowledge and ant colony optimization to reduce size of search space. Improved solution quality and computational efficiency obtained for case study.