A Multi-Objective Genetic Programming Algorithm With α Dominance and Archive for Uncertain Capacitated Arc Routing Problem

The uncertain capacitated arc routing problem (UCARP) is an important combinatorial optimization problem with many applications in the real world. Genetic programming hyper-heuristic has been successfully used to automatically evolve routing policies, which can make real-time routing decisions for U...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 27; H. 6; S. 1633 - 1647
Hauptverfasser: Wang, Shaolin, Mei, Yi, Zhang, Mengjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Archives & records
Bias
Capacitated arc routing
Combinatorial analysis
Costs
Effectiveness
Genetic algorithms
Genetic programming
hyper-heuristic
multiobjective
Optimization
Policies
Programming
Routing
Statistics
Task analysis
Training
ISSN:1089-778X, 1941-0026
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Zusammenfassung:The uncertain capacitated arc routing problem (UCARP) is an important combinatorial optimization problem with many applications in the real world. Genetic programming hyper-heuristic has been successfully used to automatically evolve routing policies, which can make real-time routing decisions for UCARPs. It is desired to evolve routing policies that are both effective and small/simple to be easily understood. The effectiveness and size are two potentially conflicting objectives. A further challenge is the objective selection bias issue, i.e., it is much more likely to obtain small but ineffective routing policies than the effective ones that are typically large. In this article, we propose a new multiobjective genetic programming algorithm to evolve effective and small routing policies. The new algorithm employs the α dominance strategy with a newly proposed α adaptation scheme to address the objective selection bias issue. In addition, it contains a new archive strategy to prevent the loss of promising individuals due to the rotation of training instances. The experimental results showed that the newly proposed algorithm can evolve significantly better routing policies than the current state-of-the-art algorithms for UCARP in terms of both effectiveness and size. We have also analyzed the evolved routing policies to show better interpretability.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3195165