Multi-Node Path Planning of Electric Tractor Based on Improved Whale Optimization Algorithm and Ant Colony Algorithm

Under the “Double Carbon” background, development of green agricultural machinery is very fast. An important factor that determines performance electric farm endurance capacity, which directly related to running path machinery. The optimized driving can reduce operating loss and extend mileage machinery, then multi-node planning helps improve working efficiency tractors. Ant Colony Optimization (ACO) often used solve problems. However, ACO has some problems, such as poor global search ability, few initial pheromones, convergence, weak optimization not conducive obtaining optimal path. This paper proposes a algorithm based on Improved Whale Optimized ACO, named IWOA-ACO. first introduces reverse learning strategy, nonlinear convergence factor, adaptive inertia weight local ability. Then, an appropriate evaluation function designed evaluate solving process obtain best fitting parameters ACO. Finally, objective function, fast stable operation requirements are achieved through optimization. simulation results show in flat environment, length energy consumption IWOA-ACO planned same those PSO-ACO, 0.61% less than WOA-ACO. In addition, bump 1.91% 4.32% 1.95% 1.25% Therefore, it helpful along with tractors, practical application value.