Du Chengtao, Zhang Jinzhong, Fang Jie
School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an, 237012, China.
Sci Rep. 2025 Jan 6;15(1):932. doi: 10.1038/s41598-024-83589-9.
The black-winged kite algorithm (BKA) constructed on the black-winged kites' migratory and predatory instincts is a revolutionary swarm intelligence method that integrates the Leader tactic with the Cauchy variation procedure to retrieve the expansive appropriate convergence solution. The essential BKA exhibits marginalized resolution efficiency, inferior assessment precision, and stagnant sensitive anticipation. To foster aggregate discovery intensity and advance widespread computational efficacy, an innovative complex-valued encoding BKA (CBKA) is presented to resolve the global optimization. The complex-valued encoding manipulates the dual-diploid configuration to encode the black-winged kite, and the actual and fictitious portions are inserted into the BKA, which transforms dual-dimensional encoding into a single-dimensional manifestation. With the inherent parallelism and consistency, the actual and fictitious portions are renewed separately for each search agent, which reinforces population pluralism, restricts discovery stagnation, extends identification area, promotes estimation excellence, advances information resources, and fosters collaboration efficiency. The CBKA not only showcases abundant flexibility and compatibility to accomplish supplementary advantages and sharpen resolution precision but also incorporates localized exploitation and universal exploration to forestall exaggerated convergence and cultivate desirable solutions. The function evaluations, engineering layouts, and adaptive infinite impulse response system identification are executed to certify the suitability and affordability of the CBKA. The experimental results manifest that the computational accomplishment and convergence productivity of the CBKA are superior to those of other comparison algorithms, the CBKA delivers noteworthy stabilization and resilience to explore superior assessment precision and swifter convergence efficiency.
基于黑翅鸢迁徙和捕食本能构建的黑翅鸢算法（BKA）是一种革命性的群体智能方法，它将领导者策略与柯西变异过程相结合，以检索扩展的合适收敛解。基本的BKA表现出边际化的求解效率、较差的评估精度和停滞敏感预期。为了提高总体发现强度并提升广泛的计算效能，提出了一种创新的复值编码BKA（CBKA）来解决全局优化问题。复值编码通过双二倍体结构对黑翅鸢进行编码，将实部和虚部插入到BKA中，将二维编码转化为一维表现形式。由于其固有的并行性和一致性，实部和虚部分别为每个搜索代理进行更新，这增强了种群多样性，限制了发现停滞，扩展了识别区域，提升了估计精度，推进了信息资源，并提高了协作效率。CBKA不仅展现出丰富的灵活性和兼容性以实现额外优势并提高求解精度，还结合了局部开发和全局探索以防止过度收敛并培育理想解。通过函数评估、工程布局和自适应无限脉冲响应系统识别来验证CBKA的适用性和可行性。实验结果表明，CBKA的计算性能和收敛效率优于其他比较算法，CBKA具有显著的稳定性和适应性，能够探索更高的评估精度和更快的收敛效率。
