多策略融合改进的金豺优化算法及其在马斯京根模型参数估计中的应用  

Multi-strategy Fusion Improved Golden Jackal Optimization Algorithm and its Application in Parameter Estimation of the Muskingum Model

作　　者：王军 王文川[1] 邱林[1] 胡小雪 WANG Jun;WANG Wen-chuan;QIU Lin;HU Xiao-xue(College of Water Resources,North China University of Water Resources and Electric Power,Zhengzhou 450046,Henan Province,China)

机构地区：[1]华北水利水电大学水资源学院,河南郑州450046

出　　处：《中国农村水利水电》2024年第2期1-7,共7页China Rural Water and Hydropower

基　　金：河南省高校科技创新团队项目(18IRTSTHN009)。

摘　　要：针对金豺优化算法在解决复杂或高维优化问题时易陷入局部最优、收敛速度慢和计算精度低等不足,提出一种基于多策略融合改进的金豺优化算法(Multi strategy fusion improved Golden Jackal Optimization Algorithm,MGJO)。首先,通过引入混沌映射策略初始化种群代替随机参数,使得算法能够在搜索空间中生成具有良好多样性的初始解,避免初始种群分布偏离最优值;其次,提出一种非线性变化的动态惯性权重使搜索过程更加符合实际情况,有效平衡了算法的全局搜索和局部搜索能力;最后,引入柯西变异的位置更新策略使其充分利用最优个体的引导作用提高种群多样性,以有效探索未知区域避免算法陷入局部最优。为了验证改进的金豺优化算法的寻优精度、收敛性能和稳定性,选择了8个不同特征的基准测试函数进行试验。结果表明,在8个基准测试函数中,改进的金豺优化算法的平均值、标准差、最优值都取得了最优的结果。此外,Wilcoxon符号秩检验的结果表明改进的金豺优化算法在统计学上是显著优越的。通过实例应用表明,基于多策略融合改进的金豺优化算法可以有效地估算出马斯京根模型的参数,优化效果明显优于粒子群优化算法、正弦余弦优化算法和金豺优化算法,进一步验证了多策略融合改进的有效性和改进算法在参数优化中的优越性,为更精确估计非线性马斯京根模型参数提供了一种有效的新方法。A multi-strategy fusion improved Golden Jackal Optimization Algorithm(MGJO)is proposed to address the shortcomings of the Golden Jackal Optimization Algorithm in solving complex or high-dimensional optimization problems,such as being prone to local optima,slow convergence speed,and low computational accuracy.Firstly,by introducing a chaotic mapping strategy to initialize the population instead of random parameters,the algorithm can generate initial solutions with good diversity in the search space and avoid the initial population distribution deviating from the optimal value.Secondly,a nonlinear dynamic inertia weight is proposed to make the search process more realistic,effectively balancing the algorithm′s global and local search capabilities.Finally,the position update strategy of Cauchy mutation is introduced to fully utilize the guiding role of the optimal individual to improve population diversity,effectively exploring unknown regions and avoiding the algorithm falling into local optima.In order to verify the optimization accuracy,convergence performance,and stability of the improved Golden Jackal Optimization Algorithm,eight benchmark test functions with different features are selected for experiments.The results show that among the 8 benchmark test functions,the improved Golden Jackal Optimization Algorithm has achieved optimal results in terms of mean,standard deviation,and optimal value.In addition,the results of Wilcoxon’s sign rank test indicate that the improved Golden Jackal Optimization Algorithm is significantly superior in statistics.Through practical applications,it shows that the Golden Jackal Optimization Algorithm based on multi-strategy fusion improvement can effectively estimate the parameters of the Muskingum Model,and the optimization effect is significantly better than the particle swarm optimization algorithm,the sine cosine optimization algorithm,and the Golden Jackal Optimization Algorithm.This further verifies the effectiveness of multi-strategy fusion improvement and the superio

关 键 词：金豺优化算法 混沌映射 动态惯性权重 柯西变异 马斯京根模型 

分 类 号：TV122[水利工程—水文学及水资源] P333.9[天文地球—水文科学]