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Information 2019, 10(1), 11; https://doi.org/10.3390/info10010011

Comparative Study of Ant Colony Algorithms for Multi-Objective Optimization

1
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
2
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
3
Department of Computer Science, IOWA State University, Ames, IA 50010, USA
4
Sam’s Club Technology Wal-mart Inc., Bentonville, AR 72712, USA
*
Author to whom correspondence should be addressed.
Received: 29 October 2018 / Revised: 12 December 2018 / Accepted: 17 December 2018 / Published: 30 December 2018
(This article belongs to the Section Artificial Intelligence)
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Abstract

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future. View Full-Text
Keywords: multi-objective optimization problem; multi-objective optimization algorithm; meta-heuristic algorithm; multi-objective ant colony optimization multi-objective optimization problem; multi-objective optimization algorithm; meta-heuristic algorithm; multi-objective ant colony optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ning, J.; Zhang, C.; Sun, P.; Feng, Y. Comparative Study of Ant Colony Algorithms for Multi-Objective Optimization. Information 2019, 10, 11.

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