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The Snow Ablation Optimizer (SAO) is an advanced optimization algorithm. However, it suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose an Enhanced Snow Ablation Optimization algorithm (ESAO). Initially, an adaptive T-distribution control strategy is employed to improve the algorithm's exploratory position adjustments, facilitating the identification of the global optimum. Furthermore, we introduce a Cauchy mutation strategy, endowing individuals with a robust capability to escape local extrema and steer the population towards more favorable directions. A leader-based boundary control strategy is also proposed to enhance the optimizer's search performance, significantly increasing the accuracy, speed, and stability of the algorithm in tackling complex problems. To validate the performance of ESAO, we utilize 29 CEC2017 benchmark functions for comparison against eight popular algorithms across various dimensions. Our algorithm ranked first in all comparisons, demonstrating ESAO's effectiveness. Additionally, to evaluate the practical applicability of the proposed method, we mathematically modeled the UAV swarm and solved the UAV swarm path planning problem using various competitor algorithms. Furthermore, we applied different competitor algorithms to two engineering design problems. The results demonstrate that ESAO performs the best. In general, ESAO outperforms its counterparts in terms of solution quality and stability, showcasing its superior application potential. |
