Hybrid Scheduling Optimization for Smart Agriculture Via INSGA-III and DynaQ Integration in Dynamic Multi-Objective Environments

Abstract

With the development of smart agriculture, agricultural production scheduling optimization has become the key to improving resource utilization efficiency and economic benefits. However, traditional methods are difficult to cope with complex decision-making needs in multi-objective and dynamic environments. In this regard, this study proposes a hybrid optimization model that integrates INSGA-III (Improved Non-dominated Sorting Genetic Algorithm III) and DynaQ (Dynamic Q-learning) to achieve multi-objective collaborative optimization and dynamic adaptability. The model adopts a dual layer architecture of "offline optimization online correction", where the upper layer generates global non supported solutions through the introduction of adaptive crossover mutation operator INSGA-III to solve multi-objective optimization problems of maximizing production, minimizing costs, and reducing carbon emissions. The lower layer uses DynaQ dynamic adjustment strategy based on MDP (Markov Decision Process) modeling to adapt to environmental changes; The scheduling rules are designed around the dual objectives of "workpiece selection machine allocation". There are three types of rules for workpiece selection, including priority for low completion, and three types of strategies for machine allocation, including efficiency priority. These are combined into nine complete rules and are based on six standardized state characteristics such as average processing completion rate and machine utilization rate for decision-making. The experiment is based on actual data from wheat planting areas, with constraints such as a water limit of 1200m ³/ha and a 15-day sowing cycle. Using adaptive genetic algorithm as a control, the optimal parameters are determined through orthogonal analysis (NIND for medium and large-scale problems is 90), and dynamic interference scenarios are introduced for verification. The results showed that compared with traditional NSGA-III, the Pareto frontier distribution index (spacing measure) of this model increased by 18.7%, the comprehensive satisfaction of the objective function reached 92.3%, the scheduling stability in dynamic environment improved by 34.5%, and the convergence speed within 100 iterations accelerated by 22%, fully demonstrating its efficiency and robustness, providing a new path for intelligent agricultural dynamic scheduling, and possessing both theoretical value and practical significance.