An efficient hybrid framework with knowledge transfer for solving capacitated vehicle routing problems
Abstract Recent progress in evolutionary multi-tasking algorithms has demonstrated potential for solving capacitated vehicle routing problems (CVRPs), though challenges persist regarding computational efficiency and knowledge transfer reliability. This study proposes a hybrid framework (HF) that add...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01920-x |
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| Summary: | Abstract Recent progress in evolutionary multi-tasking algorithms has demonstrated potential for solving capacitated vehicle routing problems (CVRPs), though challenges persist regarding computational efficiency and knowledge transfer reliability. This study proposes a hybrid framework (HF) that addresses these limitations through four key components: (1) An improved Clarke–Wright savings algorithm for rapid initialization of high-quality solutions, (2) an incremental auxiliary task formulation that optimizes knowledge transfer while minimizing computational overhead, (3) a similarity prediction strategy enabling efficient offspring selection without additional evaluations, and (4) the framework integrates enhanced search strategy and diversity maintenance strategy to prevent premature convergence. Compatible with conventional meta-heuristics, the HF demonstrates competitive optimization capability across seven CVRP benchmark suites and six real-world JD.com logistics scenarios, outperforming state-of-the-art alternatives in solution quality. |
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| ISSN: | 2199-4536 2198-6053 |