Exact and Deep Q-Network Assisted Swarm Intelligence Methods for Scheduling Multiobjective Heterogeneous Unmanned Surface Vehicles

In complex navigational environments, effective unmanned surface vehicle (USV) scheduling is critical. However, the obstacle avoidance problems are often ignored in the literature. This study addresses the multiobjective heterogeneous USV scheduling problems with obstacle avoidance. The objective is to minimize the maximum completion time and total carbon emissions. First, a mathematical model is developed to describe the concerned problems. Second, an A* algorithm is employed to obtain a path between task points with avoiding obstacles. Third, to obtain a high-quality scheduling scheme, an improved artificial bee colony (ABC) algorithm with deep Q-network (DQN) is designed. According to the problem nature, six novel rules are designed for finding high-quality solutions in initialized population. Five local search strategies are designed based on the structure of solution space. The scales of the instances and different objectives are utilized to designed a DQN, which recommends suitable local search strategies during iterations for improving convergence speed. Then, the Gurobi solver is employed to verify the proposed model. The effectiveness of the proposed strategies is verified by 13 instances with different scales. Finally, the experimental results and discussions show that the designed ABC with DQN has the strongest competitiveness among all compared algorithms.