Adaptive Genetic Algorithm Integrated with Ant Colony Optimization for Multi-Task Agricultural Machinery Scheduling

Efficient scheduling of agricultural machinery is critical for optimizing resource utilization and reducing operational costs in modern farming operations. This study proposes an Adaptive Genetic Algorithm integrated with Ant Colony Optimization (AGA-ACO) to solve the multi-task machinery scheduling problem. The problem is formulated as a Vehicle Routing Problem with Time Windows (VRPTW), considering time constraints, machinery heterogeneity, and task dependencies. The AGA-ACO algorithm employs a two-phase optimization strategy: genetic algorithms for global exploration and ant colony optimization for local refinement through pheromone-guided search. Experimental evaluation using real-world agricultural data from Hangzhou demonstrates that AGA-ACO achieves cost reductions of 5.92–10.87% compared to genetic algorithms, 5.47–7.75% compared to ant colony optimization, and 6.23–9.51% compared to particle swarm optimization, while converging with fewer iterations. The algorithm maintains stable convergence and high robustness across different farmland scales, reducing computational time while preserving solution quality. A scheduling management system integrating IoT sensors, MQTT protocols, and GIS technologies validates the practical applicability of the proposed approach. This research provides a replicable framework for agricultural machinery optimization, contributing to the advancement of sustainable and precision agriculture.