Solving a Many-Objective Crop Rotation Problem with Evolutionary Algorithms

Crop rotation consists of alternating the types of plants grown in the same place in a planned sequence to obtain improved profits and accomplish environmental outcomes. Determining optimal crop rotations is a relevant decision-making problem in agricultural farms. This work presents a seven objective crop rotation problem considering economic, social, and environmental factors and its solution using evolutionary algorithms; to this aim, an initialization procedure and genetic operators are proposed. Five multi- and many-objective evolutionary algorithms were implemented for a given problem instance, and their results were compared. The comparison shows the methods to be used as a tool for improving decision-making in crop rotations. Also, among the compared algorithms, the RVEA obtains the best values for evaluated metrics for the studied instance.

Automated summary

Crop rotation involves planting different types of crops in a planned sequence to improve profits and environmental outcomes. This study introduces a seven-objective crop rotation problem and proposes an evolutionary algorithm solution, with RVEA achieving the best evaluation metrics for the studied instance.