Framework for computationally efficient optimal crop and water allocation using ant colony optimization

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.