<p>Practical engineering optimization problems are characterized by high dimensionality, non-convexity, and non-linearity, and the use of optimizers to provide better quality solutions to the target problem in an acceptable time is a hot research topic in the field of optimal design. In this paper, inspired by the Sturnus vulgaris escape behavior, a Sturnus Vulgaris Escape Algorithm (SVEA) is proposed to provide a high-performance optimizer for complex optimization problems. The algorithm is composed of exploration and exploitation strategies, controlled by fixed parameters. The exploration strategies include the High-Altitude Escape Strategy and Wave Escape Strategy 1, while the exploitation strategies consist of the Cordon Line Strategy and Wave Escape Strategy 2. The High-Altitude Escape Strategy enhances exploration capabilities by reorganizing subgroups, preventing the leader and optimal individuals from overlapping, and avoiding collisions between individuals. The Cordon Line Strategy conducts refined searches around high-value regions, further improving optimization precision. Wave Escape Strategies 1 and 2 help the population escape local optima and prevent over-spreading. The performance of SVEA is evaluated through the employment of 23 benchmark test functions and the CEC2017 test set, with a subsequent comparison undertaken with nine statE − of-thE − art meta-heuristic algorithms. The outcomes of this evaluation demonstrate that SVEA attains the top ranking and is identified as the best-performing algorithm across all test sets. A statistical analysis reveals that the SVEA solution set exhibits superior performance in comparison to the other algorithms, with the discrepancy in performance being deemed to be statistically significant. Finally, the algorithm is applied to five real-world engineering problems, all providing optimal solutions while satisfying the constraints.</p>