An improved reinforcement-immune algorithm for agricultural resource allocation optimization

Abstract

Due to the decreasing and limited agricultural resources, developing techniques for agricultural resource allocation optimization has received increasing attention in recent years. In this paper, an improved reinforcement-immune algorithm (RIA) is proposed to realize an efficient and intelligent optimization in agricultural resource allocation and routing problem of the delivery vehicles. By combining the strong self-adaptability and goal-driven performance of reinforcement learning with the antibody diversity and strong global search capability of immune optimization, RIA has better performance not only on the convergence property but also on the effectiveness of finding the optimal solution. First, we introduce Table Q as the action policy. Each element in Table Q means the action score that we choose delivery vehicle v to allocate resource t of supply center s to field i. Table Q is initialized according to the distance between the field and the supply center. Second, we update Table Q to learn good genetic information according to the optimal antibody after each iteration. Each antibody represents an allocation scheme. Third, at the stage of antibody mutation, we use Table Q to guide antibodies, which makes the algorithm adaptive and goal-driven. Finally, simulation results illustrate that the proposed algorithm is effective in improving the climbing performance, searching ability and finding the optimal solution.