Black-Winged Kite Algorithm Integrating Opposition-Based Learning and Quasi-Newton Strategy

To address the deficiencies in global search capability and population diversity decline of the black-winged kite algorithm (BKA), this paper proposes an enhanced black-winged kite algorithm integrating opposition-based learning and quasi-Newton strategy (OQBKA). The algorithm introduces a mirror imaging strategy based on convex lens imaging (MOBL) during the migration phase to enhance the population's spatial distribution and assist individuals in escaping local optima. In later iterations, it incorporates the quasi-Newton method to enhance local optimization precision and convergence performance. Ablation studies on the CEC2017 benchmark set confirm the strong complementarity between the two integrated strategies, with OQBKA achieving an average ranking of 1.34 across all 29 test functions. Comparative experiments on the CEC2022 benchmark suite further verify its superior exploration-exploitation balance and optimization accuracy: under 10- and 20-dimensional settings, OQBKA attains the best average rankings of 2.5 and 2.17 across all 12 test functions, outperforming ten state-of-the-art metaheuristic algorithms. Moreover, evaluations on three constrained engineering design problems, including step-cone pulley optimization, corrugated bulkhead design, and reactor network design, demonstrate the practicality and robustness of the proposed approach in generating feasible solutions under complex constraints.