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What makes the dynamic capacitated Arc routing problem hard to solve: insights from fitness landscape analysis

Authors:

Leandro L. Minku,

Bernhard Sendhoff,

Xin YaoAuthors Info & Claims

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

Pages 305 - 313

https://doi.org/10.1145/3512290.3528756

Published: 08 July 2022 Publication History

Abstract

The Capacitated Arc Routing Problem (CARP) aims at assigning vehicles to serve tasks which are located at different arcs in a graph. However, the originally planned routes are easily affected by different dynamic events like newly added tasks. This gives rise to Dynamic CARP (DCARP) instances, which need to be efficiently optimized for new high-quality service plans in a short time. However, it is unknown which dynamic events make DCARP instances especially hard to solve. Therefore, in this paper, we provide an investigation of the influence of different dynamic events on DCARP instances from the perspective of fitness landscape analysis based on a recently proposed hybrid local search (HyLS) algorithm. We generate a large set of DCARP instances based on a variety of dynamic events and analyze the fitness landscape of these instances using several different measures such as fitness correlation length. From the empirical results we conclude that cost-related events have no significant impact on the difficulty of DCARP instances, but instances which require more new vehicles to serve the remaining tasks are harder to solve. These insights improve our understanding of the DCARP instances and pave the way for future work on improving the performance of DCARP algorithms.

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Cited By

Tong HMinku LMenzel SSendhoff BYao X(2024)Evaluating Meta-Heuristic Algorithms for Dynamic Capacitated Arc Routing Problems Based on a Novel Lower Bound MethodIEEE Computational Intelligence Magazine10.1109/MCI.2024.344021319:4(31-44)Online publication date: Nov-2024
https://doi.org/10.1109/MCI.2024.3440213
Kenny ARay TLimmer SSingh HRodemann TOlhofer M(2024)A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search BehaviourApplications of Evolutionary Computation10.1007/978-3-031-56855-8_7(115-129)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56855-8_7
Pei JTong HLiu JMei YYao XSilva SPaquete L(2023)Local Optima Correlation Assisted Adaptive Operator SelectionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590399(339-347)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590399

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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.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 08 July 2022

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Honda Research Institute Europe (HRI-EU)
Guangdong Provincial Key Laboratory
Shenzhen Science and Technology Program
Program for Guangdong Introducing Innovative and Enterpreneurial Teams
Research Institute of Trustworthy Autonomous Systems (RITAS)

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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

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Cited By

Tong HMinku LMenzel SSendhoff BYao X(2024)Evaluating Meta-Heuristic Algorithms for Dynamic Capacitated Arc Routing Problems Based on a Novel Lower Bound MethodIEEE Computational Intelligence Magazine10.1109/MCI.2024.344021319:4(31-44)Online publication date: Nov-2024
https://doi.org/10.1109/MCI.2024.3440213
Kenny ARay TLimmer SSingh HRodemann TOlhofer M(2024)A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search BehaviourApplications of Evolutionary Computation10.1007/978-3-031-56855-8_7(115-129)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56855-8_7
Pei JTong HLiu JMei YYao XSilva SPaquete L(2023)Local Optima Correlation Assisted Adaptive Operator SelectionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590399(339-347)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590399
Nagy ZWerner-Stark ÁDulai T(2022)An Artificial Bee Colony Algorithm for Static and Dynamic Capacitated Arc Routing ProblemsMathematics10.3390/math1013220510:13(2205)Online publication date: 24-Jun-2022
https://doi.org/10.3390/math10132205

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