Through the analysis of the data, using the A - branching factor analysis the different behaviors of ant system algorithm and improved algorithm in different parameters sets. verified the optimization of the search behavior through the use of the candidate list. 通过对仿真实验数据的分析,使用λ-分支因子(BF)法分析了基本蚁群算法与改进算法在不同的参数设置下的搜索行为的差异,验证了候选列表对于算法搜索行为的优化。
Moreover, the analysis has indicated that the asymptotic heuristic branching factor is same as the brute-force branching factor. 分析还表明渐进启发分支因数与遍历分支因数相同。
Nested factors depends on the branching factors, and within different levels of the branching factor, there exist different nested factors. 嵌套因子依赖于分支因子(BF)的水平,在分支因子(BF)的不同水平下,存在着不同的嵌套因子。
Traditional analysis use accuracy as character of the heuristic function and the function's effect is to decrease the actual branching factor. 传统的分析以启发值的精确性作为启发函数的特征,启发函数的作用相当于减小有效的分支因数。