Therefore, it can improve the solution quality for multitasks. The main advantage of the approach is to allow transferrable knowledge between tasks. Multifactorial Evolutionary Algorithm (MFEA) is a variant of the Evolutionary Algorithm (EA), aiming to solve multiple factorial tasks simultaneously. However, the literature does not have an algorithm that simultaneously solves two problems. Existing algorithms have been developed for solving two problems independently in the literature. The difference between the TRPTW and TSPTW is that the TRPTW takes a customer-oriented view, whereas the TSPTW is server-oriented. In these two problems, the deliveries are made during a specific time window given by the customers. The TRPTW wants to minimize the sum of travel durations between a depot and customer locations, while the TSPTW aims to minimize the total time to visit all customers.
We studied two problems called the Traveling Repairman Problem (TRPTW) and Traveling Salesman Problem (TSPTW) with time windows. Solving optimization problems simultaneously: the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm.
For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Computer Science Department, School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam DOI 10.7717/peerj-cs.1192 Published Accepted Received Academic Editor Yilun Shang Subject Areas Algorithms and Analysis of Algorithms, Artificial Intelligence Keywords MFEA, TSPTW, TRPTW, Metaheuristic, Algorithm, Combinatorial optimization Copyright © 2023 Ban and Pham Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed.
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