[1]王凯欣,田云娜,李奕轩.求解复杂函数及工程问题的改进黑翅鸢优化算法[J].计算机技术与发展,2025,(10):191-198.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0150]
　WANG Kai-xin,TIAN Yun-na,LI Yi-xuan.An Improved Black-winged Kite Optimization Algorithm for Solving Complex Functions and Engineering Problem[J].,2025,(10):191-198.[doi:10.20165/j.cnki.ISSN1673-629X.2025.0150]
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求解复杂函数及工程问题的改进黑翅鸢优化算法

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年10期
页码:
191-198
栏目:
新型计算应用系统
出版日期:
2025-10-10

文章信息/Info

Title:
An Improved Black-winged Kite Optimization Algorithm for Solving Complex Functions and Engineering Problem
文章编号:
1673-629X(2025)10-0191-08
作者:
王凯欣; 田云娜; 李奕轩
延安大学数学与计算机科学学院,陕西延安 716000
Author(s):
WANG Kai-xin; TIAN Yun-na; LI Yi-xuan
School of Mathematics and Computer Science,Yan'an University,Yan'an 716000,China
关键词:
黑翅鸢优化算法; Levy飞行策略; 自适应随机余弦振荡因子; 函数优化; 工程设计优化问题
Keywords:
black-winged kite optimization algorithm;  Levy flight strategy;  adaptive stochastic cosine oscillation factor;  function optimization; engineering design optimization problem
分类号:
TP301.6
DOI:
10.20165/j.cnki.ISSN1673-629X.2025.0150
摘要:
针对黑翅鸢优化算法存在全局搜索能力弱、易陷入局部最优以及全局探索与局部开发能力不平衡的问题,提出一种多策略改进的黑翅鸢优化算法。在种群攻击行为阶段引入Levy飞行策略,增强算法的全局探索能力,降低局部桎梏概率,有助于算法跳出局部最优;在种群迁徙行为阶段引入自适应随机余弦振荡因子,平衡算法的全局探索与局部开发能力。为了验证该算法的寻优性能,将其与7个优化算法在CEC2017和CEC2022测试套件上进行测试,同时进行Wilcoxon秩和检验和Friedman检验来分析算法之间的显著性差异。实验结果表明,该算法的寻优精度、稳定性以及收敛性能明显优于对比算法,尤其在高维复杂函数下,表现出更加优越的寻优性能。为了进一步验证该算法的可行性与实用性,将其应用于压力容器设计问题上,验证了该算法在处理实际优化问题时具有一定的优越性。
Abstract:
Aiming at the problems that the black-winged kite optimization algorithm has weak global exploration ability,easy to fall into the local optimum and imbalance between global search and local exploitation ability,a multi-strategy improved black-winged kite opti-mization algorithm is proposed. The Levy flight strategy is introduced in the stage of population attacking behavior to enhance the global exploration ability of the algorithm and reduce the local shackling probability,which helps the algorithm to jump out of the local optimum. The adaptive stochastic cosine oscillator is introduced in the stage of population migrating behavior to balance the global searching and local exploitation ability of the algorithm. In order to verify the optimization search performance of the proposed algorithm,the proposed algorithm and seven optimization algorithms are tested on the CEC2017 and CEC2022 test suites,and the Wilcoxon rank sum test and Friedman test are also performed to analyze the significance differences between the algorithms. The experimental results show that the proposed algorithm's optimization accuracy,stability,and convergence performance are significantly better than that of the comparison algorithm,especially under the high-dimensional complex function,which exhibits more superior opti-mization performance. In order to further verify the feasibility and practicability of the proposed algorithm,it is applied to the design of pressure vessel,and the superiority of the proposed algorithm in dealing with practical optimization problems is verified.
更新日期/Last Update: 2025-10-10