全局搜索和云模型动态扰动的鱼鹰优化算法  

Osprey optimization algorithm based on global search and dynamic disturbance of cloud model

作　　者：左锋琴 张达敏 邓佳欣 文裕杰 ZUO Feng-qin;ZHANG Da-min;DENG Jia-xin;WEN Yu-jie(School of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳贵州550025

出　　处：《计算机工程与设计》2025年第4期966-973,共8页Computer Engineering and Design

基　　金：国家自然科学基金项目(62166006);贵州省科学技术基金项目(黔科合基础[2020]1Y254)。

摘　　要：针对鱼鹰优化算法(OOA)收敛速度慢和稳定性低等问题,提出一种全局搜索和云模型动态扰动的鱼鹰优化算法(GDOOA)。利用正态云模型动态扰动策略更新种群最优解,加快算法收敛速度;在算法探索阶段,采用自适应更新机制平衡全局搜索和局部开发能力,提高算法的收敛精度;在开发阶段,引入全局优化导引策略为鱼鹰个体提供3种更新机制,提升个体的灵活性和算法的全局搜素能力。在8个基准测试函数和Wilcoxon秩和检验中进行对比实验,其结果表明,GDOOA在性能上具有优势。工程问题测试结果表明,GDOOA同时适用于实际工程应用问题。Aiming at the low convergence speed and low stability of the osprey optimization algorithm(OOA),an osprey optimization algorithm based on global search and dynamic disturbance of cloud model(GDOOA)was proposed.The dynamic perturbation strategy of normal cloud model was used to update the optimal solution of the population and to accelerate the convergence speed of the algorithm.In the exploration stage of the algorithm,the adaptive update mechanism was used to balance the global search and local development capabilities to improve the convergence accuracy of the algorithm.In the development phase,a global optimisation guidance strategy was introduced to provide three update mechanisms for osprey individuals,which improved the flexibility of the individual and the global search capability of the algorithm.Through comparison experiments in eight benchmark test functions and Wilcoxon rank sum test,the results show that GDOOA has advantages in performances.And by testing in engineering problems,the results show that GDOOA is also suitable for real engineering application problems.

关 键 词：鱼鹰优化算法 云模型动态扰动 自适应更新机制 全局优化导引策略 基准测试 秩和检验 工程问题 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]