多策略改进的鱼鹰优化算法及其应用  

Improved osprey optimization algorithm based on multiple strategies and its application

作　　者：李奕轩 田云娜[1] LI Yixuan;TIAN Yunna(College of Mathematics and Computer Science,Yan’an University,Yan’an 716000,China)

机构地区：[1]延安大学数学与计算机科学学院,陕西延安716000

出　　处：《延安大学学报（自然科学版）》2024年第4期99-108,共10页Journal of Yan'an University：Natural Science Edition

基　　金：国家自然科学基金项目(61763046,62041212)。

摘　　要：针对鱼鹰优化算法存在收敛速度慢、收敛精度低以及易陷入局部最优的问题,提出一种多策略融合改进的鱼鹰优化算法。首先采用Cubic混沌映射初始化种群,增加种群的多样性;其次引入互利共生策略,加强搜索个体之间的信息交流,提高算法跳出局部最优的能力;最后利用透镜成像反向学习策略,增强算法跳出局部最优的能力,平衡算法的探索与开发。通过18个基准测试函数求解对比分析、统计测试,该算法在收敛速度、寻优精度以及稳定性上都得到明显提升。此外,通过压力容器设计问题的实验对比,进一步验证了所提算法在实际工程应用中的适用性。Aiming at the problems of slow convergence speed,low convergence accuracy and easy to fall into local optimum of osprey optimization algorithm,an improved osprey optimization algorithm based on multi-strategy fusion was proposed.Firstly,the Cubic chaotic mapping is used to initialize the population to increase the diversity of the population.Secondly,the mutual benefit strategy is introduced to strengthen the information exchange between search individuals and improve the ability of the algorithm to jump out of the local optimum.Finally,the lens imaging reverse learning strategy is used to enhance the ability of the algorithm to jump out of the local optimum and balance the exploration and exploitation of the algorithm.Through the comparative analysis and statistical test of 18 benchmark test functions,the algorithm has been significantly improved in convergence speed,optimization accuracy and stability.In addition,through the experimental comparison of pressure vessel design problems,the applicability of the algorithm in practical engineering applications is further verified.

关 键 词：鱼鹰优化算法 Cubic混沌映射 互利共生 透镜成像反向学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]