Modeling and Optimization Algorithm for Rotational Irrigation Group Based on Connectivity Weighted Index

Abstract

Abstract Manual calculations for rotational irrigation groups are inefficient, and a single objective cannot provide farmers with diverse options. Based on considering both engineering standards and practical needs, this study designs a connectivity index and proposes a new model with the optimization objectives of balanced flow and minimum connectivity. To address infeasible solutions and constraint issues, A hybrid genetic algorithm based on variable neighborhoods is adopted to enhance the search capability of the classical non-dominated sorting genetic algorithm. The effectiveness and universality of the model and algorithm are verified through multiple real-world cases. The research shows that the proposed multi-objective model is more practical than a single-objective model, and farmers can be provided with diverse Pareto solutions by adjusting the weight index ω. The variable neighborhood search algorithm enhances the search capability within the radius threshold, improves population diversity, and analyzes the radius threshold and branch pipe flow parameters to explore factors that affect algorithm performance and indicators.