Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/31117
Title: Measuring the Performance of Ant Colony Optimization Algorithms for the Dynamic Traveling Salesman Problem
Authors: Mavrovouniotis, Michalis 
Anastasiadou, Maria N. 
Hadjimitsis, Diofantos G. 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: ant colony optimization;dynamic optimization;traveling salesman problem
Issue Date: 1-Dec-2023
Source: Algorithms, 2023, vol. 16, iss. 12
Volume: 16
Issue: 12
Project: Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence 
Journal: Algorithms 
Abstract: Ant colony optimization (ACO) has proven its adaptation capabilities on optimization problems with dynamic environments. In this work, the dynamic traveling salesman problem (DTSP) is used as the base problem to generate dynamic test cases. Two types of dynamic changes for the DTSP are considered: (1) node changes and (2) weight changes. In the experiments, ACO algorithms are systematically compared in different DTSP test cases. Statistical tests are performed using the arithmetic mean and standard deviation of ACO algorithms, which is the standard method of comparing ACO algorithms. To complement the comparisons, the quantiles of the distribution are also used to measure the peak-, average-, and bad-case performance of ACO algorithms. The experimental results demonstrate some advantages of using quantiles for evaluating the performance of ACO algorithms in some DTSP test cases.
URI: https://hdl.handle.net/20.500.14279/31117
ISSN: 19994893
DOI: 10.3390/a16120545
Rights: © by the authors
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : ERATOSTHENES Centre of Excellence 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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