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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
algorithms-16-00545.pdf | Full text | 758.1 kB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
1
checked on Mar 14, 2024
Page view(s)
140
Last Week
0
0
Last month
8
8
checked on Nov 21, 2024
Download(s)
46
checked on Nov 21, 2024
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License