Optimization problems are prevalent in various fields of science and engineering, with several real-world applications characterized by high dimensionality and complex search landscapes. Starfish optimization algorithm (SFOA) is a recently optimizer inspired by swarm intelligence, which is effective for numerical optimization, but it may encounter premature and local convergence for complex optimization problems. To address these challenges, this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm (MCPSFOA). The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA, which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer (CPO). This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces. To further prevent premature convergence, MCPSFOA incorporates Lévy flight, leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima. Subsequently, Gaussian mutation is applied for precise solution tuning, introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation. Notably, the population diversity enhancement mechanism periodically identifies and resets stagnant individuals, thereby consistently revitalizing population variety throughout the optimization process. MCPSFOA is rigorously evaluated on 24 classical benchmark functions (including high-dimensional cases), the CEC2017 suite, and the CEC2022 suite. MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208, 2.310 and 2.417 on these benchmark functions, outperforming 11 state-of-the-art algorithms. Furthermore, the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases, where it also yields excellent results. In conclusion, MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions, but also a practical tool for solving real-world optimization problems.

To address traffic safety concerns on highway bridge, this study investigated the anti-collision performance of inclined railings using LS-DYNA finite element software, with heavy trucks as the collision subject. The effects of railing inclination angle, thickness, and material on protective performance were systematically analyzed. Results indicate that the inclination angle significantly influences performance: Larger angles increase collision forces, while angles exceeding 10° compromise the railing's guidance function, though a 10° inclination remains effective. Guidance performance deteriorates notably when railing thickness reaches 3 mm or 4 mm. Aluminum alloy railings outperform Q235 steel due to superior rebound deformation, enhancing protection. Although inclined railings effectively improve bridge wind resistance, excessive inclination angles substantially compromise collision protection. Under design parameters of 10° inclination angle and thickness exceeding 4 mm, aluminum alloy railings demonstrate optimal balance between functional requirements.