【摘要】 针对野狗优化算法（Dingo Optimization Algorithm,DOA）收敛速度偏慢和寻优精度较低等问题，提出一种基于PWLCM和秃鹰机制改进的野狗优化算法（Improved Dingo Optimization Algorithm,IDOA）。首先，使用具有遍历性的分段线性混沌映射（Piecewise Linear Chaotic Map,PWLCM）初始化野狗种群，有效增加野狗种群多样性。其次，在迫害策略中引入秃鹰俯冲机制，加快野狗捕获猎物的速度，加强算法探索局部的能力。最后，在食腐策略引入螺旋搜索因子，增强算法的局部寻优能力，提升算法的寻优速度和求解精度。仿真实验数据、消融实验以及Wilcoxon秩和检验均表明，与其他对比算法相比，提出的IDOA在所有测试函数上有着更佳的寻优速度以及寻优精度；与其他改进的野狗优化算法相比，所提出的IDOA展现出更好的整体性能。更多还原

【Abstract】 Aiming at the problems of slow convergence speed and low optimization accuracy of dingo optimization algorithm(DOA),an improved dingo optimization algorithm(IDOA)based on PWLCM and the bald eagle’s swooping mechanism is proposed. Firstly,a piecewise linear chaotic map with eriodicity is used to initialize the dingo population,effectively increasing the diversity of the dingo population. Secondly,the bald eagle’s swooping mechanism is introduced into the persecution strategy to accelerate the speed of prey capture and strengthen the ability of the algorithm to explore local areas. Finally,the spiral search factor is introduced into the scavenger strategy to enhance the local development and exploration ability of the algorithm,so as to further improve the optimization speed and accuracy of the algorithm. Simulation experiment data,ablation experiment and Wilcoxon rank sum test all show that the proposed IDOA has better optimization speed and optimization accuracy than other comparison algorithms;Compared to other improved dingo optimization algorithms,the proposed IDOA shows better overall performance.更多还原