改进北方苍鹰优化算法的收敛性及其性能对比分析

Comparative Analysis of Convergence and Performance of Improved Northern Goshawk Optimization Algorithm

针对北方苍鹰优化算法存在易陷入局部最优的问题,提出一种改进北方苍鹰优化算法(INGO)。在种群初始化阶段,引入佳点集方法映射到搜索空间,提高了种群的多样性以及避免了早熟;在位置更新阶段,加入鱼鹰局部勘探位置更新策略和自适应惯性权重因子,增强了全局勘探和局部开发能力同时提升算法的收敛速度和收敛精度;建立INGO算法的北方苍鹰捕猎过程Markov链模型,证明了全局收敛性。通过实验仿真与六种经典智能算法进行对比分析验证INGO算法的有效性,并对INGO算法进行收敛曲线和Wilcoxon秩和检验分析,实验结果表明INGO算法能够有效地避免陷入局部最优,具有较强的收敛精度和鲁棒性。为了进一步描述INGO算法的实际应用能力,将该算法成功应用于工程设计问题中,验证了INGO算法在实际应用中的有效性。

In order to solve the problems of the northern goshawk optimization(NGO)algorithm,which quickly falls into local optimal,an improved northern goshawk optimization(INGO)algorithm is proposed in this paper.Firstly,during the population initialization stage,the good point set method is introduced to map to the search space,improving the populations diversity and avoiding precociousness.In the position update stage,the osprey local exploration position update strategy and adaptive inertia weight factor are added to enhance global exploration and local development capabilities and improve the convergence speed and accuracy of the algorithm.Secondly,the Markov chain model of the hunting process of the northern goshawk,based on the INGO algorithm,is established to prove the global convergence.The effectiveness of the INGO algorithm is verified through experimental simulation and comparative analysis with six classical intelligent algorithms.The INGO algorithms convergence curve and Wilcoxon rank sum test analysis are carried out.Experimental results show that the INGO algorithm can effectively avoid falling into local optimality and has vital convergence accuracy and robustness.Finally,in order to further characterize the practical application capability of the INGO algorithm,the algorithm is successfully applied to engineering design problems to verify the effectiveness of the INGO algorithm in practical applications.