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A bi-level encoding scheme for the clustered shortest-path tree problem in multifactorial optimization
Engineering Applications of Artificial Intelligence ( IF 7.5 ) Pub Date : 2021-02-12 , DOI: 10.1016/j.engappai.2021.104187
Huynh Thi Thanh Binh , Ta Bao Thang , Nguyen Duc Thai , Pham Dinh Thanh

The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life. Recently, some Multifactorial Evolutionary Algorithms (MFEAs) have been introduced to deal with the CluSPT, but these researches still have some shortcomings, such as evolution operators only perform on complete graphs and huge resource consumption for finding the solution on large search spaces. To overcome these limitations, this paper describes an MFEA-based approach to solve the CluSPT. The proposed algorithm utilizes Dijkstra’s algorithm to construct the spanning trees in clusters while using evolutionary operators for building the spanning tree connecting clusters. This approach takes advantage of both exact and approximate algorithms, so it enables the algorithm to function efficiently on complete and sparse graphs alike. Furthermore, evolutionary operators such as individual encoding and decoding methods are also designed with great consideration regarding performance and memory usage. We have included proof of the repairing method’s efficacy in ensuring all solutions are valid. We have conducted tests on various types of Euclidean instances to assess the effectiveness of the proposed algorithm and methods. Experiment results point out the effectiveness of the proposed algorithm existing heuristic algorithms in most of the test cases. The impact of the proposed MFEA was analyzed, and a possible influential factor that may be useful for further study was also pointed out.



中文翻译:

多因素优化中集群最短路径树问题的双层编码方案

聚类最短路径树问题(CluSPT)在现实生活中的各种优化问题中起着重要作用。最近,已经引入了一些多因子进化算法(MFEA)来处理CluSPT,但是这些研究仍然存在一些缺陷,例如,进化算子只能在完整的图形上执行,并且在大型搜索空间中寻找解决方案需要大量资源。为了克服这些限制,本文介绍了一种基于MFEA的方法来解决CluSPT。所提出的算法利用Dijkstra算法在集群中构建生成树,同时使用进化算子构建连接集群的生成树。这种方法同时利用了精确算法和近似算法,因此,它使算法能够在完整和稀疏图上高效运行。此外,还设计了诸如单独的编码和解码方法之类的进化运算符,并考虑到性能和内存使用情况。我们已提供了修复方法在确保所有解决方案均有效方面的功效证明。我们对各种类型的欧几里得实例进行了测试,以评估所提出算法和方法的有效性。实验结果表明,该算法在大多数测试用例中均有效。分析了提议的MFEA的影响,并指出了可能对进一步研究有用的影响因素。诸如单独的编码和解码方法之类的进化运算符在设计时还考虑了性能和内存使用情况。我们已提供了修复方法在确保所有解决方案均有效方面的功效证明。我们对各种类型的欧几里得实例进行了测试,以评估所提出算法和方法的有效性。实验结果表明,该算法在大多数测试用例中均有效。分析了提议的MFEA的影响,并指出了可能对进一步研究有用的影响因素。诸如单独的编码和解码方法之类的进化运算符在设计时还考虑了性能和内存使用情况。我们已提供了修复方法在确保所有解决方案均有效方面的功效证明。我们对各种类型的欧几里得实例进行了测试,以评估所提出算法和方法的有效性。实验结果表明,该算法在大多数测试用例中均有效。分析了提议的MFEA的影响,并指出了可能对进一步研究有用的影响因素。我们对各种类型的欧几里得实例进行了测试,以评估所提出算法和方法的有效性。实验结果表明,该算法在大多数测试用例中均有效。分析了提议的MFEA的影响,并指出了可能对进一步研究有用的影响因素。我们对各种类型的欧几里得实例进行了测试,以评估所提出算法和方法的有效性。实验结果表明,该算法在大多数测试用例中均有效。分析了提议的MFEA的影响,并指出了可能对进一步研究有用的影响因素。

更新日期:2021-02-12
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