基于随机贪心算法的启发式最短路径树问题
A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem
摘要：
随机贪心算法（RGAs）是解决结构不清楚的问题以及结合了随机过程和贪心算法的组合优化问题的有趣方法。本文提出了一种新的算法，结合RGA的主要特点和最短路径树算法（SPTA）来处理的最短路径树问题（CluSPT）。该算法利用SPTA算法确定簇内最短路径树，结合RGA的特点和SPTA的搜索策略构造连接簇的边。为了评估所提出的算法的性能，欧几里德基准选择。实验研究表明，该算法的优势，与现有的一些算法相比。我们还分析了参数对算法性能的影响。
英文摘要：
Randomized Greedy Algorithms (RGAs) are interesting approaches to solve problems whose structures are not well understood as well as problems in combinatorial optimization which incorporate the random processes and the greedy algorithms. This paper introduces a new algorithm that combines the major features of RGAs and Shortest Path Tree Algorithm (SPTA) to deal with the Clustered Shortest-Path Tree Problem (CluSPT). In our algorithm, SPTA is used to determine the shortest path tree in each cluster while the combination between characteristics of the RGAs and search strategy of SPTA is used to constructed the edges connecting clusters. To evaluate the performance of the proposed algorithm, Euclidean benchmarks are selected. The experimental investigations show the strengths of the proposed algorithm in comparison with some existing algorithms. We also analyze the influence of the parameters on the performance of the algorithm.
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