Secretary Bird Escape Strategy-Guided Dynamic PID Search Algorithm

The PID Search Algorithm (PSA) is an effective optimization method but suffers from limited global search capability, often leading to premature convergence. To address these limitations, this paper proposes the Secretary Bird Escape Strategy-Guided Dynamic PID Search Algorithm (SB-EDPSA). First, the Kent chaotic map is employed to initialize the population, providing a strong foundation for global optimization. Second, PSA's proportional, integral, and derivative coefficients are dynamically adjusted to balance exploration and exploitation. Additionally, the secretary bird escape strategy enhances the algorithm's global search capability and ability to escape local optima. The effectiveness of SB-EDPSA is validated through comparisons with five state-of-the-art algorithms on CEC2015 benchmark functions. Experimental results show that SB-EDPSA outperforms other advanced algorithms in both solution quality and stability, demonstrating its competitiveness.