A sterna migration algorithm-based efficient bionic engineering optimization algorithm

This study proposes the Sterna Migration Algorithm (StMA), a novel metaheuristic optimization method that integrates multi-cluster sectoral diffusion, leader-follower dynamics, adaptive perturbation regulation, and a multi-phase termination mechanism to achieve a dynamic balance between global exploration and local exploitation. StMA was systematically evaluated on the CEC2023 benchmark functions and the CEC2014 constrained engineering design problems, with comparisons against several mainstream population-based algorithms through 30 independent runs per problem. Results show that, on the CEC2014 benchmarks, StMA significantly outperforms competitors in 23 of 30 functions (Wilcoxon rank-sum test, α = 0.05, p < 0.05), achieving 100% superiority on unimodal functions (F1-F5), 75% on basic multimodal functions (F6-F10), and 61.5% on hybrid and composite functions (F11-F30). Average generations to convergence decrease by 37.2%, and relative errors drop by 14.7%-92.3%, indicating improved convergence efficiency and solution accuracy. On the CEC2023 benchmarks (F1-F11), StMA attains lower mean values and reduced standard deviations for most functions, demonstrating stable convergence and enhanced global search capability.In engineering optimization, StMA achieves the best overall performance across six representative design problems (RC6, RC9, RC13, RC16, RC17, RC20), validating its robustness and adaptability to complex constraints. The algorithm's combination of biologically inspired mechanisms and innovative multi-phase control strategies distinguishes it from existing methods, offering both high solution quality and computational efficiency. These results collectively confirm StMA's effectiveness in high-dimensional, multimodal, and heavily constrained optimization scenarios, while providing a solid foundation for next-generation metaheuristic algorithm design.