Adaptive memory-based opposition and midpoint mutation in black winged kite algorithm for global optimization and engineering applications

Metaheuristic algorithms play a vital role in addressing complex and nonlinear optimization problems. This study proposes an enhanced variant of the Black-winged Kite Algorithm (BKA), termed Adaptive Memory-based Opposition and Midpoint Mutation in BKA (AMOMM-BKA), developed to improve population diversity and convergence accuracy, particularly for complex optimization problems. The proposed framework integrates four complementary strategies to balance exploration and exploitation effectively. Blended Opposition-Based Learning (BOBL) combines classical opposition with population-mean guidance to adaptively expand the search space, while historical reflective opposition exploits individual memory to guide the search toward promising regions. Random opposition introduces controlled randomness to preserve population diversity and prevent premature convergence, and midpoint-based mutation directs individuals toward the midpoint between elite and peer solutions, enhancing focused exploration and convergence precision. AMOMM-BKA was evaluated using three CEC benchmark suites (CEC2005, CEC2019, and CEC2022), and its performance was compared with four categories of existing optimization algorithms:(i) widely cited classical optimizers, such as PSO and GWO; (ii) recently developed algorithms, including GJO, SO, SCSO, and AVOA; (iii) high-performance optimizers, such as CMAES and SHADE; and (iv) improved variants of BKA, including CBKA, IBKA, and QOBLBKA. Moreover, its successful application to four mechanical and structural engineering design problems further validates the algorithm's effectiveness and practical relevance. The statistical analysis, including the Friedman rank test and Wilcoxon test, were conducted on the experimental results to verify the robustness and significance of the findings. AMOMM-BKA consistently demonstrated superior performance, achieving the top rank with an average score of 1.78 approximately 56.18% better than the second-best algorithm, SHADE (average rank: 4.56) highlighting its remarkable convergence rate, solution accuracy, and robustness across diverse optimization.