## 6 Conclusions  
To address the inefficiency and limited capability of traditional path planning algorithms in complex environments, this paper proposes a multi-strategy enhanced Chim-panzee Optimization Algorithm (MSECHOA). By incorporating multiple strategies, MSECHOA significantly improves optimization efficiency and adaptability. Comparative experiments with other classic optimization algorithms demonstrate that MSECHOA provides an efficient and reliable solution for unmanned delivery vehicle path planning. The main conclusions are as follows:  
1. A chimpanzee algorithm (MSECHOA) combining hybrid good point set chaos initialization, benchmark weight strategy, Gaussian-modulated cosine factor, and Global Exploration Enhancer (GEE) strategies was proposed. Compared to the original chimpanzee algorithm, which has limited global search capability and is prone to becoming trapped in local optima when dealing with complex path planning, MSECHOA effectively addresses these limitations, enhancing the algorithm's global search capability and convergence accuracy. 2. The experimental results show that MSECHOA performs excellently across eleven benchmark test functions, particularly in optimization accuracy, convergence speed, and global search capability, surpassing traditional and other improved algorithms. In the simulation experiments for delivery unmanned vehicle path planning, MSECHOA not only generated superior path planning schemes in both simple and complex environments, but also exhibited outstanding performance in terms of computational efficiency and algorithm stability. Compared to other algorithms, MSECHOA effectively balances global search and local exploitation capabilities, avoiding the pitfalls of local optima, demonstrating strong adaptability and robustness, making it suitable for handling complex and dynamic real-world scenarios. The MSECHOA algorithm provides an efficient and reliable solution for delivery unmanned vehicle path planning, with broad application prospects. In addition to its proven effectiveness in simple and complex map environments, the algorithm has also been validated in experiments simulating more realistic logistics scenarios involving random tasks and dynamic obstacles. Future research can focus on further optimizing the algorithm's computational efficiency, improving its adaptability in dynamic and real-world logistics environments, and enhancing its performance in simulations of more realistic logistics scenarios. These efforts aim to refine the algorithm's practical utility and accelerate the development and widespread adoption of unmanned delivery technology. Moreover, the superior optimization performance of the MSECHOA algorithm also demonstrates extensive application potential in other practical scenarios. For instance, in logistics distribution center scheduling, the algorithm can optimize vehicle and cargo allocation to improve logistical efficiency. In intelligent warehouse path planning, it can assist robots in generating optimal paths within dynamic storage environments, thereby reducing operation time. In industrial production scheduling, it provides efficient scheduling solutions for multi-process manufacturing tasks, thereby enhancing production efficiency. Despite its outstanding performance in various optimization problems, MSECHOA still has certain limitations. For example, the algorithm's capability in handling real-time path planning in dynamic environments requires further validation and improvement. Additionally, its adaptability to high-dimensional and complex environments also hasroom for enhancement.