A bi-level encoding scheme for the clustered shortest-path tree problem in multifactorial optimization

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