Water‐Saving Crop Planning Using Multiple Objective Chaos Particle Swarm Optimization for Sustainable Agricultural and Soil Resources Development

Establishing a water-saving planting structure is necessary for the arid, water-deficient regions of northern China and of the world. Optimizing and adjusting a water-saving agricultural planting structure is a typical semi-structured, multi-level, multi-objective group decision-making problem. Therefore, optimization can be best achieved with a swarm intelligence algorithm. We build an optimization model for a water-saving planting structure with four target functions: (1) maximum total net output, (2) total grain yield, (3) ecological benefits, and (4) water productivity. The decision variable is the yearly seeded area of different crops, and its restrictions are the farmland area, the agricultural water resources, and the needs of the people and other farming-related industries. Multiple objective particle swarm optimization (MOPSO) is an efficient optimization method, but its main shortcoming is that it can easily fall into a local optimum. Multiple objective chaos particle swarm optimization (MOCPSO) will greatly improve the searching performance of the algorithm by placing chaos technology with the advantages of ergodicity into MOPSO. When MOCPSO is used to solve the multi-objective optimization model in the middle portion of the Heihe River basin, the results show that MOCPSO has the advantages of a high convergence speed and a tendency not to fall easily into a local optimum. After adopting a water-saving agricultural planting structure, irrigation water would be reduced by about 7%, which would provide tangible economic, social, and ecological benefits for sustainable agricultural development.