摘要
针对传统鲸鱼优化算法收敛速度慢和容易陷入局部最优等问题,提出了基于惯性混沌的改进鲸鱼优化算法(ICIWOA)。利用混沌Tent映射机制初始化种群改善了种群多样性和初始解质量。通过设计非均匀衰减收敛因子更新机制,使收敛因子以不同的速度衰减,加速了算法收敛。设计了自适应惯性权重位置更新机制,均衡了全局搜索能力和局部开发能力。通过实验证明了算法具有更好跳离局部最优的性能,能提高搜索精度和收敛速度的结论。通过网络安全态势预测验证了算法的可行性和有效性。
In the light of the problems of slow convergence speed and easy to fall into local optimization of traditional whale optimization algorithm, an inertia chaos improved whale optimization algorithm(ICIWOA) is proposed. The chaotic tent mapping mechanism is used to initialize the population, which improves the diversity of the population and the quality of the initial solution.By designing the updating mechanism of non-uniform attenuation convergence factor, the convergence factor decays at different speeds, which accelerates the convergence of the algorithm. An adaptive inertia weight position update mechanism is designed to balance the ability of global search and local development. Experiments show that the improved algorithm has better performance of jumping away from the local optimum, and can improve the search accuracy and convergence speed. The feasibility and effectiveness of the improved algorithm are verified by network security situation prediction.
作者
丁智
肖宇
DING Zhi;XIAO Yu(School of Computer and Information Engineering,Bengbu University,Bengbu 233030,China)
出处
《新乡学院学报》
2022年第6期44-49,62,共7页
Journal of Xinxiang University
基金
安徽省课程思政建设示范课程项目(2020szsfkc0580)
蚌埠学院新工科教学研究项目(2020XGKJY9)。
关键词
鲸鱼优化算法
混沌映射
收敛因子
惯性权重
网络安全
whale optimization algorithm
chaos mapping
convergence factor
inertia weight
network security
