Sturnus vulgaris escape algorithm and its application to mechanical design.

Area of Science:

• Engineering Optimization
• Computational Intelligence
• Meta-heuristic Algorithms

Background:

• Engineering optimization problems often present high dimensionality, non-convexity, and non-linearity.
• Developing efficient optimizers is crucial for achieving high-quality solutions within practical timeframes.

Purpose of the Study:

• To introduce a novel meta-heuristic algorithm, the Sturnus Vulgaris Escape Algorithm (SVEA), inspired by bird behavior.
• To provide a high-performance optimizer for complex engineering optimization tasks.

Main Methods:

• The SVEA incorporates distinct exploration (High-Altitude Escape, Wave Escape 1) and exploitation (Cordon Line, Wave Escape 2) strategies.
• Algorithm performance was validated using 23 benchmark functions, the CEC2017 test set, and five real-world engineering problems.
• SVEA was compared against nine state-of-the-art meta-heuristic algorithms.

Main Results:

• SVEA achieved the top performance ranking across all evaluated test functions and datasets.
• Statistical analysis confirmed the significantly superior performance of SVEA compared to other algorithms.
• The algorithm successfully provided optimal solutions for all five real-world engineering problems while adhering to constraints.

Conclusions:

• The Sturnus Vulgaris Escape Algorithm (SVEA) demonstrates exceptional effectiveness for complex engineering optimization.
• SVEA offers a robust and efficient approach, outperforming existing meta-heuristic methods.
• The algorithm's practical applicability is validated through successful real-world engineering problem-solving.