abstract

A new hybrid ant colony algorithms for the traveling thief problem

Authors:

Wiem Zouari,

Ines Alaya,

Moncef TaginaAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 95 - 96

https://doi.org/10.1145/3319619.3326785

Published: 13 July 2019 Publication History

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Abstract

The Traveling Thief Problem (TTP) is a new problem recently proposed in the literature. The TTP combines two well-known optimization problems: the knapsack problem (KP) and the travelling salesman problem (TSP). In this paper, new hybrid ant colony algorithms are presented. We study and compare different approaches for solving the TTP. The first approach is a centralized and static metaheuristic, the second is a dynamic metaheuristic and the third is a distributed metaheuristic. The obtained results prove that our algorithms are efficient for instances of TTP.

References

[1]

Mohammad Reza Bonyadi, Zbigniew Michalewicz, and Luigi Barone. 2013. The travelling thief problem: The first step in the transition from theoretical problems to realistic problems. In 2013 IEEE Congress on Evolutionary Computation. IEEE, 1037--1044.

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[2]

Wiem Zouari, Ines Alaya, and Moncef Tagina. 2017. A Hybrid Ant Colony Algorithm with a Local Search for the Strongly Correlated Knapsack Problem. In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE, 527--533.

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[3]

Wiem Zouari, Ines Alaya, and Moncef Tagina. 2018. A Comparative Study of a Hybrid Ant Colony Algorithm MMACS for the Strongly Correlated Knapsack Problem. Advances in Science, Technology and Engineering Systems Journal 3, 6 (2018), 1--22.

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[4]

Marco Dorigo and Thomas Stützle. 2019. Ant colony optimization: overview and recent advances. In Handbook of metaheuristics. Springer, 311--351.

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[5]

Sergey Polyakovskiy, Mohammad Reza Bonyadi, Markus Wagner, Zbigniew Michalewicz, and Frank Neumann. 2014. A comprehensive benchmark set and heuristics for the traveling thief problem. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. ACM, 477--484.

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Xiang YGuo JJiang CMa HLiu M(2024)Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief ProblemApplied Sciences10.3390/app1417746814:17(7468)Online publication date: 23-Aug-2024
https://doi.org/10.3390/app14177468
Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/3641109Online publication date: 17-Jan-2024
https://doi.org/10.1145/3641109
Pathirage Don TNeumann ANeumann FLi XHandl J(2024)The Chance Constrained Travelling Thief Problem: Problem Formulations and AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654014(214-222)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654014
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Index Terms

A new hybrid ant colony algorithms for the traveling thief problem
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. General and reference
  1. Cross-computing tools and techniques
    1. Empirical studies

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Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 13 July 2019

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Xiang YGuo JJiang CMa HLiu M(2024)Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief ProblemApplied Sciences10.3390/app1417746814:17(7468)Online publication date: 23-Aug-2024
https://doi.org/10.3390/app14177468
Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/3641109Online publication date: 17-Jan-2024
https://doi.org/10.1145/3641109
Pathirage Don TNeumann ANeumann FLi XHandl J(2024)The Chance Constrained Travelling Thief Problem: Problem Formulations and AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654014(214-222)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654014
Bossek JWagner MKrawiec K(2021)Generating instances with performance differences for more than just two algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3463165(1423-1432)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3463165
Ali IEssam DKasmarik K(2020)Differential Evolution Algorithm for Multiple Inter-dependent Components Traveling Thief Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185692(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185692
Chagas JBlank JWagner MSouza MDeb K(2020)A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problemJournal of Heuristics10.1007/s10732-020-09457-7Online publication date: 20-Sep-2020
https://doi.org/10.1007/s10732-020-09457-7

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Socially inspired algorithms for the travelling thief problem

Population-based vs. Single-solution Heuristics for the Travelling Thief Problem

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

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