Optimum crop planning using multi-objective differential evolution algorithm

Abstract

This study presents a Multi-objective Differential Evolution Algorithm (MDEA) which is a family of Evolutionary Algorithms (EAs) that can be used to solve multi-objective constrained optimization problem. MDEA technique is adapted to crop planning in a farmland in the Vaalharts Irrigation Scheme (VIS) in South Africa. The objectives of the model are to maximize the total Net Profit (NF) in monetary terms (South African Rand, ZAR) generated on the farm by planting 4 different crops, maximize total planting area (m2) and minimize the irrigation water use (m3). Numerical results produce non-dominated solutions which converge to Pareto optimal fronts. From the results, the total net profit, irrigation water and planting area range from ZAR 272 390 to ZAR 1 001 900, 317 030 to 689 310 m3 and 260 937 to 757 040 m2, respectively. From the analysis of the results, it is suggested that the irrigation water supplied to the study area be increased to enable farmers to generate more profit. Also, the irrigation technology should be changed to water economic technology. It is found that the proposed MDEA can be used for solving crop planning problem and generate non-dominated solutions from where a farmer can select a solution that suits his particular situation. © 2009 Asian Netwotk for Scientific Information.