Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework

Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimization problems. However, most metaheuristic algorithms have been designed manually by researchers of different expertise without a consistent framework. This article proposes a general search f...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 27; H. 4; S. 1072 - 1084
Hauptverfasser: Yi, Wenjie, Qu, Rong, Jiao, Licheng, Niu, Ben
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation models
Algorithms
Automated algorithm design
Automation
Combinatorial analysis
Evolutionary computation
general search framework
Heuristic algorithms
Heuristic methods
Machine learning
Machine learning algorithms
metaheuristic
Metaheuristics
Optimization
Reinforcement learning
Search problems
Vehicle routing
vehicle routing problem
ISSN:1089-778X, 1941-0026
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Zusammenfassung:Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimization problems. However, most metaheuristic algorithms have been designed manually by researchers of different expertise without a consistent framework. This article proposes a general search framework (GSF) to formulate in a unified way a range of different metaheuristics. With generic algorithmic components, including selection heuristics and evolution operators, the unified GSF aims to serve as the basis of analyzing algorithmic components for automated algorithm design. With the established new GSF, two reinforcement learning (RL)-based methods, deep <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-network based and proximal policy optimization-based methods, have been developed to automatically design a new general population-based algorithm. The proposed RL-based methods are able to intelligently select and combine appropriate algorithmic components during different stages of the optimization process. The effectiveness and generalization of the proposed RL-based methods are validated comprehensively across different benchmark instances of the capacitated vehicle routing problem with time windows. This study contributes to making a key step toward automated algorithm design with a general framework supporting fundamental analysis by effective machine learning.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3197298