This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a mul...This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.展开更多
提出了一种基于块特性与自适应搜索窗口的运动估计算法(Motion estimation algorithm based on blockcharacteristic and adaptive search window,MBC-ASW),该算法在充分利用视频图像的时间、空间相关性的同时,根据运动向量的统计特性,...提出了一种基于块特性与自适应搜索窗口的运动估计算法(Motion estimation algorithm based on blockcharacteristic and adaptive search window,MBC-ASW),该算法在充分利用视频图像的时间、空间相关性的同时,根据运动向量的统计特性,分别在帧层和块层进行自适应搜索窗口的粗调与微调,并且针对不同的块进行相应的编码处理。在PC上利用C语言实验验证了该算法的可行性,评估了其性能,并与经典的全搜索算法(Fullsearch motion estimation,FS)、三步搜索算法(Three step search,TSS)、新三步搜索算法(New three stepsearch,NTSS)和钻石搜索算法(Diamond search,DS)进行了详细的比较,该算法在性能和效率上均有较大程度的提高,对不同的序列具有较强的自适应性。展开更多
文摘This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.
文摘提出了一种基于块特性与自适应搜索窗口的运动估计算法(Motion estimation algorithm based on blockcharacteristic and adaptive search window,MBC-ASW),该算法在充分利用视频图像的时间、空间相关性的同时,根据运动向量的统计特性,分别在帧层和块层进行自适应搜索窗口的粗调与微调,并且针对不同的块进行相应的编码处理。在PC上利用C语言实验验证了该算法的可行性,评估了其性能,并与经典的全搜索算法(Fullsearch motion estimation,FS)、三步搜索算法(Three step search,TSS)、新三步搜索算法(New three stepsearch,NTSS)和钻石搜索算法(Diamond search,DS)进行了详细的比较,该算法在性能和效率上均有较大程度的提高,对不同的序列具有较强的自适应性。
