A High-Performance Parallel Algorithm for Multi-Objective Integer Optimization
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866914325539586048 |
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| author | Prinz, Kathrin Nemesch, Levin Ruzika, Stefan |
| author_facet | Prinz, Kathrin Nemesch, Levin Ruzika, Stefan |
| contents | Multi-objective integer optimization problems are hard to solve, mainly because the number of nondominated images is often extremely large. We present the first exact algorithm, called PEA, that fully utilizes the multicore architecture of modern hardware. By exploiting the structure of the parameter set of the underlying scalarization, PEA can use a high number of threads while avoiding the usual pitfalls of parallel computing. It is highly scalable and easy to implement. As a result, PEA can solve much larger instances than previous state-of-the-art algorithms. Besides, PEA has a sound theoretical foundation. Unlike other existing parallel algorithms, it always solves the same number of scalarization problems as comparable sequential algorithms. We demonstrate the potential of PEA in a computational study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11872 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A High-Performance Parallel Algorithm for Multi-Objective Integer Optimization Prinz, Kathrin Nemesch, Levin Ruzika, Stefan Optimization and Control Multi-objective integer optimization problems are hard to solve, mainly because the number of nondominated images is often extremely large. We present the first exact algorithm, called PEA, that fully utilizes the multicore architecture of modern hardware. By exploiting the structure of the parameter set of the underlying scalarization, PEA can use a high number of threads while avoiding the usual pitfalls of parallel computing. It is highly scalable and easy to implement. As a result, PEA can solve much larger instances than previous state-of-the-art algorithms. Besides, PEA has a sound theoretical foundation. Unlike other existing parallel algorithms, it always solves the same number of scalarization problems as comparable sequential algorithms. We demonstrate the potential of PEA in a computational study. |
| title | A High-Performance Parallel Algorithm for Multi-Objective Integer Optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2602.11872 |