Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

TL;DR

This paper introduces a multifactorial evolutionary algorithm tailored for the NP-hard Clustered Minimum Routing Cost problem, focusing on innovative operators and cost calculation methods to improve solution efficiency.

Contribution

It presents a novel application of MFEA to CluMRCT, with specialized crossover, mutation, and cost evaluation techniques for better approximation.

Findings

Effective on large datasets

Improves solution quality

Reduces resource consumption

Abstract

Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instances