Variable Search Stepsize for Randomized Local Search in Multi-Objective Combinatorial Optimization
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917251220766720 |
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| author | Ren, Xuepeng Wang, Maocai Dai, Guangming Liang, Zimin Liu, Qianrong Yang, Shengxiang Li, Miqing |
| author_facet | Ren, Xuepeng Wang, Maocai Dai, Guangming Liang, Zimin Liu, Qianrong Yang, Shengxiang Li, Miqing |
| contents | Over the past two decades, research in evolutionary multi-objective optimization has predominantly focused on continuous domains, with comparatively limited attention given to multi-objective combinatorial optimization problems (MOCOPs). Combinatorial problems differ significantly from continuous ones in terms of problem structure and landscape. Recent studies have shown that on MOCOPs multi-objective evolutionary algorithms (MOEAs) can even be outperformed by simple randomised local search. Starting with a randomly sampled solution in search space, randomised local search iteratively draws a random solution (from an archive) to perform local variation within its neighbourhood. However, in most existing methods, the local variation relies on a fixed neighbourhood, which limits exploration and makes the search easy to get trapped in local optima. In this paper, we present a simple yet effective local search method, called variable stepsize randomized local search (VS-RLS), which adjusts the stepsize during the search. VS-RLS transitions gradually from a broad, exploratory search in the early phases to a more focused, fine-grained search as the search progresses. We demonstrate the effectiveness and generalizability of VS-RLS through extensive evaluations against local search and MOEAs methods on diverse MOCOPs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05675 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Variable Search Stepsize for Randomized Local Search in Multi-Objective Combinatorial Optimization Ren, Xuepeng Wang, Maocai Dai, Guangming Liang, Zimin Liu, Qianrong Yang, Shengxiang Li, Miqing Neural and Evolutionary Computing Over the past two decades, research in evolutionary multi-objective optimization has predominantly focused on continuous domains, with comparatively limited attention given to multi-objective combinatorial optimization problems (MOCOPs). Combinatorial problems differ significantly from continuous ones in terms of problem structure and landscape. Recent studies have shown that on MOCOPs multi-objective evolutionary algorithms (MOEAs) can even be outperformed by simple randomised local search. Starting with a randomly sampled solution in search space, randomised local search iteratively draws a random solution (from an archive) to perform local variation within its neighbourhood. However, in most existing methods, the local variation relies on a fixed neighbourhood, which limits exploration and makes the search easy to get trapped in local optima. In this paper, we present a simple yet effective local search method, called variable stepsize randomized local search (VS-RLS), which adjusts the stepsize during the search. VS-RLS transitions gradually from a broad, exploratory search in the early phases to a more focused, fine-grained search as the search progresses. We demonstrate the effectiveness and generalizability of VS-RLS through extensive evaluations against local search and MOEAs methods on diverse MOCOPs. |
| title | Variable Search Stepsize for Randomized Local Search in Multi-Objective Combinatorial Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2602.05675 |