In recent years, China's express delivery market has developed rapidly in the context of a booming economy. However, logistics costs are still high, which will affect the decision-making and policy making of relevant departments. Therefore, it is essential to optimize the last-mile assignment problem (LMAP) to meet the consumer’s demand for delivery time and reduce economic expenditure. The LMAP of express delivery requires multiple packages to be delivered to different destinations. Finding the path with the minimum delivery cost and time is an NP-hard problem, and it is impossible to obtain the optimal solution by enumerating all possible answers. This study proposes a new express delivery path planning method based on a clone adaptive ant colony optimization (CAACO) to find suboptimal solutions. Moreover, a new distribution cost fitness function constructed by weighing the economic expenditure and time of express delivery is designed. Specifically, a new adaptive operator and a novel clone operator are also designed to accelerate the speed of convergence. Finally, by comparing the performance of CAACO with ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA), the effectiveness of CAACO in solving the express LAMP is verified. In the simulation results, it is obvious that the economic expenditure and time of express delivery based on the CAACO are lower than ACO, SA, and GA, and the convergence speed is also faster than the SA and GA. It can be seen that CAACO has valuable benefits in solving LMAP.

Since the low cost and high flexibility, wireless automatic meter reading network (WAMRN) is widely used by utility companies to realize automatic collection and transmission of remote energy consumption information. Considering that WAMRN is composed of several wireless communication nodes, the lifetime of the network will be affected by factors such as the changeable deployment environment and the limited energy of nodes. Thus, a novel niche quantum ant colony-based WAMRN clustering optimization method is proposed in this paper to address the problem of how to make full use of the limited energy to extend network lifetime and improve data transmission efficiency. In the proposed approach, a clustering model of WAMRN is defined; moreover, an improved Niche Quantum Ant Colony Optimization (NQACO) is proposed to optimize the model therefore to obtain an optimal clustering scheme, which can help WAMRN reduce unnecessary energy loss to achieve the purpose of extending the lifetime of the entire network. To verify the performance of the proposed method, NQACO is compared with some popular clustering methods, i.e., GA and SA, under different scenarios. The results show that under the premise of ensuring network communication, NQACO is superior to the other two methods in reducing the total energy consumption and prolonging the network lifetime.