Application of Multi-Objective Differential Evolution Algorithm (MDEA) to Irrigation Planning

Farmers in a water scarce environment have a problem of maximizing total income from farming. Irrigation planning is very important to prevent crop failures. DE, an evolutionary algorithm, which is a stochastic parallel direct search evolution algorithm known to be fast and robust in numerical optimization, is extended to multi-objective problems in this study. The new algorithm named multi-objective differential evolution algorithm (MDEA) adjusts the selection scheme of traditional DE to solve multi-objective problems. The algorithm also modifies the domination criteria for the population. The offspring generated in subsequent generations are improved before domination check is performed on the population in the final generation. MDEA is tested on three benchmark problems and later applied to an irrigation planning. The algorithm achieves the two goals in multi-objective optimization algorithm which are to discover solutions as close to the Pareto-front as possible and to find solutions as diverse as possible in the obtained non-dominated front.