With the rapid development of complex engineering systems, many real-world optimization problems are characterized by high dimensionality, strong nonlinearity, and variable coupling. To address these challenges, this paper proposes a Public Management–Augmented Multi-Strategy Adaptive Enterprise Development Optimization algorithm (PMAED), which integrates adaptive differential evolution, an eigen-based rotated search strategy, and a hierarchical performance governance mechanism to enhance convergence efficiency and robustness. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that PMAED achieves superior performance across different problem types and dimensionalities. In the Friedman ranking test, PMAED consistently obtains the best average rank (1.90 and 1.60 on CEC2020; 2.00 and 1.92 on CEC2022 for 10D and 20D, respectively), outperforming all compared algorithms. The Wilcoxon rank-sum test further confirms that PMAED achieves statistically significant improvements on the majority of benchmark functions. In high-dimensional scenarios, PMAED shows remarkable optimization accuracy, for example, achieving a mean fitness value of 1.15 × 103 on the 20-dimensional CEC2020 F1 function, significantly outperforming classical methods. In addition, PMAED is applied to a three-dimensional UAV path planning problem. The results show that the proposed method achieves the lowest average path cost (277.62) and the smallest standard deviation among all algorithms, indicating superior stability and reliability. The planned paths are smoother, safer, and more efficient compared to those generated by other methods. Overall, the proposed PMAED provides a robust and efficient solution for complex continuous optimization problems and demonstrates strong potential for real-world engineering applications.
Niu et al. (Fri,) studied this question.