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Comparison of Decisions Quality of Heuristic Methods with Limited Depth-First Search Techniques in the Graph Shortest Path Problem

机译:图最短路径问题中启发式方法与有限深度优先搜索技术的决策质量比较

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The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.
机译:本文通过有限的深度优先搜索技术获取决策,解决了启发式方法有效性的分析问题。本文根据组合搜索树的分支数限制和用于解决该问题的分析子树深度限制,简要介绍了这组方法。考虑使用基于BOINC平台的具有伪随机结构的图形样本以及选定的顶点和圆弧数的计算样本来比较实验数据以估计溶液质量的方法。它还显示了获得的实验结果的描述,这些实验结果允许根据问题的大小和约束的力量来确定启发式方法的选定子集的最佳用法区域。结果表明,所考虑的这两种方法在所选问题上无效,并且明显劣于蚁群优化方法及其对组合收益率的修改所提供的解决方案的质量。

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