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

TL;DR

This paper introduces a bi-level encoding scheme within a multifactorial evolutionary algorithm to efficiently solve the Clustered Shortest-Path Tree Problem, improving performance on large and sparse graphs.

Contribution

It presents a novel MFEA-based approach combining exact and approximate algorithms with specialized encoding for CluSPT, addressing previous limitations in resource use and graph types.

Findings

The proposed algorithm outperforms existing heuristics in most test cases.

It effectively handles both complete and sparse graphs.

The method demonstrates good scalability and solution validity.

Abstract

The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life. Recently, some Multifactorial Evolutionary Algorithm (MFEA) have been introduced to deal with the CluSPT, however these researches still have some shortcomings such as evolution operators only perform on complete graphs, huge resource consumption for finding the solution on large search spaces. To overcome these limitations, this paper describes a 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 a proof on 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.