research-article

On the use of quality diversity algorithms for the traveling thief problem

Authors:

Adel Nikfarjam,

Frank NeumannAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 260 - 268

https://doi.org/10.1145/3512290.3528752

Published: 08 July 2022 Publication History

Abstract

In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem. There is an inter-dependency between the sub-problems, making it impossible to solve such a problem by focusing on only one component. The traveling thief problem (TTP) belongs to this category and is formed by the integration of the traveling salesperson problem (TSP) and the knapsack problem (KP). In this paper, we investigate the inter-dependency of the TSP and the KP by means of quality diversity (QD) approaches. QD algorithms provide a powerful tool not only to obtain high-quality solutions but also to illustrate the distribution of high-performing solutions in the behavioural space. We introduce a MAP-Elite based evolutionary algorithm using well-known TSP and KP search operators, taking the TSP and KP score as behavioural descriptor. Afterwards, we conduct comprehensive experimental studies that show the usefulness of using the QD approach applied to the TTP. First, we provide insights regarding high-quality TTP solutions in the TSP/KP behavioural space. Afterwards, we show that better solutions for the TTP can be obtained by using our QD approach and it can improve the best-known solution for a wide range of TTP instances used for benchmarking in the literature.

References

[1]

Bradley Alexander, James Kortman, and Aneta Neumann. 2017. Evolution of artistic image variants through feature based diversity optimisation. In GECCO. ACM, 171--178.

[2]

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 IEEE Congress on Evolutionary Computation. IEEE, 1037--1044.

[3]

Mohammad Reza Bonyadi, Zbigniew Michalewicz, Michal Roman Przybylek, and Adam Wierzbicki. 2014. Socially inspired algorithms for the travelling thief problem. In GECCO. ACM, 421--428.

[4]

Mohammad Reza Bonyadi, Zbigniew Michalewicz, Markus Wagner, and Frank Neumann. 2019. Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions. In Optimization in Industry. Springer, 13--30.

[5]

Jakob Bossek, Aneta Neumann, and Frank Neumann. 2021. Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms. In GECCO. ACM, 556--564.

[6]

Jakob Bossek and Frank Neumann. 2021. Evolutionary diversity optimization and the minimum spanning tree problem. In GECCO. ACM, 198--206.

[7]

Jonatas B. C. Chagas and Markus Wagner. 2020. A weighted-sum method for solving the bi-objective traveling thief problem. CoRR abs/2011.05081 (2020).

[8]

Konstantinos I. Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades, and Jean-Baptiste Mouret. 2020. Quality-Diversity Optimization: a novel branch of stochastic optimization. CoRR abs/2012.04322 (2020).

[9]

Jeff Clune, Jean-Baptiste Mouret, and Hod Lipson. 2013. Summary of "the evolutionary origins of modularity". In GECCO (Companion). ACM, 23--24.

[10]

Georges A Croes. 1958. A method for solving traveling-sales man problems. Operations research 6, 6 (1958), 791--812.

[11]

Antoine Cully. 2020. Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters. CoRR abs/2007.05352 (2020).

[12]

Antoine Cully and Jean-Baptiste Mouret. 2013. Behavioral repertoire learning in robotics. In GECCO. ACM, 175--182.

[13]

Anh Viet Do, Jakob Bossek, Aneta Neumann, and Frank Neumann. 2020. Evolving diverse sets of tours for the travelling salesperson problem. In GECCO. ACM, 681--689.

[14]

Anh Viet Do, Mingyu Guo, Aneta Neumann, and Frank Neumann. 2021. Analysis of evolutionary diversity optimisation for permutation problems. In GECCO. ACM, 574--582.

[15]

Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K. Hoover, and Stefanos Nikolaidis. 2021. Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network. In AAAI. AAAI Press, 5922--5930.

[16]

Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, and Amy K. Hoover. 2020. Covariance matrix adaptation for the rapid illumination of behavior space. In GECCO. ACM, 94--102.

[17]

Theodoros Galanos, Antonios Liapis, Georgios N. Yannakakis, and Reinhard Koenig. 2021. ARCH-Elites: quality-diversity for urban design. In GECCO Companion. ACM, 313--314.

[18]

Wanru Gao, Samadhi Nallaperuma, and Frank Neumann. 2021. Feature-Based Diversity Optimization for Problem Instance Classification. Evol. Comput. 29, 1 (2021), 107--128.

[19]

Joel Lehman and Kenneth O. Stanley. 2011. Abandoning Objectives: Evolution Through the Search for Novelty Alone. Evol. Comput. 19, 2 (2011), 189--223.

Digital Library

[20]

Alenrex Maity and Swagatam Das. 2020. Efficient hybrid local search heuristics for solving the travelling thief problem. Appl. Soft Comput. 93 (2020), 106284.

[21]

Yuichi Nagata and Shigenobu Kobayashi. 2013. A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem. INFORM J. Comput. 25, 2 (2013), 346--363.

Digital Library

[22]

Aneta Neumann, Jakob Bossek, and Frank Neumann. 2021. Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions. In GECCO. ACM, 261--269.

[23]

Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, and Markus Wagner. 2018. Discrepancy-based evolutionary diversity optimization. In GECCO. ACM, 991--998.

[24]

Aneta Neumann, Wanru Gao, Markus Wagner, and Frank Neumann. 2019. Evolutionary diversity optimization using multi-objective indicators. In GECCO. ACM, 837--845.

[25]

Frank Neumann, Sergey Polyakovskiy, Martin Skutella, Leen Stougie, and Junhua Wu. 2018. A Fully Polynomial Time Approximation Scheme for Packing While Traveling. In ALGOCLOUD (Lecture Notes in Computer Science, Vol. 11409). Springer, 59--72.

[26]

Adel Nikfarjam, Jakob Bossek, Aneta Neumann, and Frank Neumann. 2021. Computing diverse sets of high quality TSP tours by EAX-based evolutionary diversity optimisation. In FOGA. ACM, 9:1--9:11.

[27]

Adel Nikfarjam, Jakob Bossek, Aneta Neumann, and Frank Neumann. 2021. Entropy-based evolutionary diversity optimisation for the traveling salesperson problem. In GECCO. ACM, 600--608.

[28]

Adel Nikfarjam, Aneta Neumann, and Frank Neumann. 2022. Evolutionary Diversity Optimisation for The Traveling Thief Problem. CoRR abs/2204.02709 (2022).

[29]

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 GECCO. ACM, 477-484.

[30]

Justin K. Pugh, Lisa B. Soros, and Kenneth O. Stanley. 2016. Quality Diversity: A New Frontier for Evolutionary Computation. Frontiers Robotics AI 3 (2016), 40.

[31]

Justin K. Pugh, Lisa B. Soros, Paul A. Szerlip, and Kenneth O. Stanley. 2015. Confronting the Challenge of Quality Diversity. In GECCO. ACM, 967--974.

[32]

Nemanja Rakicevic, Antoine Cully, and Petar Kormushev. 2021. Policy manifold search: exploring the manifold hypothesis for diversity-based neuroevolution. In GECCO. ACM, 901--909.

[33]

Kirby Steckel and Jacob Schrum. 2021. Illuminating the space of beatable lode runner levels produced by various generative adversarial networks. In GECCO Companion. ACM, 111--112.

[34]

Paolo Toth. 1980. Dynamic programming algorithms for the Zero-One Knapsack Problem. Computing 25, 1 (1980), 29--45.

[35]

Tamara Ulrich and Lothar Thiele. 2011. Maximizing population diversity in single-objective optimization. In GECCO. ACM, 641--648.

[36]

Vassilis Vassiliades, Konstantinos I. Chatzilygeroudis, and Jean-Baptiste Mouret. 2018. Using Centroidal Voronoi Tessellations to Scale Up the Multidimensional Archive of Phenotypic Elites Algorithm. IEEE Trans. Evol. Comput. 22, 4 (2018), 623--630.

[37]

Markus Wagner. 2016. Stealing Items More Efficiently with Ants: A Swarm Intelligence Approach to the Travelling Thief Problem. In ANTS Conference (Lecture Notes in Computer Science, Vol. 9882). Springer, 273--281.

[38]

Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, and Frank Hutter. 2018. A case study of algorithm selection for the traveling thief problem. J. Heuristics 24, 3 (2018), 295--320.

Digital Library

[39]

Junhua Wu, Markus Wagner, Sergey Polyakovskiy, and Frank Neumann. 2017. Exact Approaches for the Travelling Thief Problem. In SEAL (Lecture Notes in Computer Science, Vol. 10593). Springer, 110--121.

[40]

Mohamed El Yafrani and Belaïd Ahiod. 2015. Cosolver2B: An efficient local search heuristic for the Travelling Thief Problem. In AICCSA. IEEE Computer Society, 1--5.

[41]

Mohamed El Yafrani and Belaïd Ahiod. 2018. Efficiently solving the Traveling Thief Problem using hill climbing and simulated annealing. Inf. Sci. 432 (2018), 231--244.

[42]

Enrico Zardini, Davide Zappetti, Davide Zambrano, Giovanni Iacca, and Dario Floreano. 2021. Seeking quality diversity in evolutionary co-design of morphology and control of soft tensegrity modular robots. In GECCO. ACM, 189--197.

[43]

Wiem Zouari, Inès Alaya, and Moncef Tagina. 2019. A new hybrid ant colony algorithms for the traveling thief problem. In GECCO (Companion). ACM, 95--96.

Cited By

Qian CXue KWang RLarson K(2024)Quality-diversity algorithms can provably be helpful for optimizationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/773(6994-7002)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/773
Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/36411094:2(1-22)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3641109
Gounder SNeumann FNeumann ALi XHandl J(2024)Evolutionary Diversity Optimisation for Sparse Directed Communication NetworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654184(1237-1245)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654184
Show More Cited By

Recommendations

Evolutionary diversity optimisation for the traveling thief problem
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem. This can provide the practitioners with invaluable information about the solution space and ...
Approximate Approaches to the Traveling Thief Problem
GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

This study addresses the recently introduced Traveling Thief Problem (TTP) which combines the classical Traveling Salesman Problem (TSP) with the 0-1 Knapsack Problem (KP). The problem consists of a set of cities, each containing a set of available ...
Socially inspired algorithms for the travelling thief problem
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Many real-world problems are composed of two or more problems that are interdependent on each other. The interaction of such problems usually is quite complex and solving each problem separately cannot guarantee the optimal solution for the overall ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

July 2022

1472 pages

ISBN:9781450392372

DOI:10.1145/3512290

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Australian Research Council (ARC)

Conference

GECCO '22

Sponsor:

SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
194
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)4

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Qian CXue KWang RLarson K(2024)Quality-diversity algorithms can provably be helpful for optimizationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/773(6994-7002)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/773
Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/36411094:2(1-22)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3641109
Gounder SNeumann FNeumann ALi XHandl J(2024)Evolutionary Diversity Optimisation for Sparse Directed Communication NetworksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654184(1237-1245)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654184
Schmidbauer MOpris ABossek JNeumann FSudholt DLi XHandl J(2024)Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean ConjunctionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654160(1614-1622)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654160
Nikfarjam AStanford TNeumann ADumuid DNeumann FLi XHandl J(2024)Quality Diversity Approaches for Time-Use Optimisation to Improve Health OutcomesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654085(1318-1326)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654085
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 JSudholt D(2024)Runtime Analysis of Quality Diversity AlgorithmsAlgorithmica10.1007/s00453-024-01254-z86:10(3252-3283)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00453-024-01254-z
Antipov DNeumann ANeumann F(2024)Local Optima in Diversity Optimization: Non-trivial Offspring Population is EssentialParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_12(181-196)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_12
Gadea Harder JNeumann ANeumann F(2024)Analysis of Evolutionary Diversity Optimisation for the Maximum Matching ProblemParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_10(149-165)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70071-2_10
Nikfarjam ARothenberger RNeumann FFriedrich TSilva SPaquete L(2023)Evolutionary Diversity Optimisation in Constructing Satisfying AssignmentsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590517(938-945)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590517
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten