An Improved Grey Wolf Algorithm for Scheduling Agricultural Harvesting Robots to Minimize Energy Consumption and Time Cost

Aimed at improving the efficiency of agricultural harvesting robots to perform picking tasks, this paper takes the weighted sum of energy consumption and time cost as the objective function and designs an improved grey wolf optimization (GWO) algorithm. According to the characteristics of the problem, a discrete integer coding method is adopted first, and the Clarke & Wright's algorithm is used to generate a high-quality and high-diversity initial population. Secondly, a nonlinear convergence factor and destruction reconstruction strategy based on correlation are introduced to balance the global search and local search capabilities. The experimental results show that the proposed algorithm has higher optimization accuracy and faster optimization speed than the well-known genetic algorithm and tabu search in the literature.