TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912795367309312 |
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| author | Soykan, Bulent Mondesire, Sean Rabadi, Ghaith |
| author_facet | Soykan, Bulent Mondesire, Sean Rabadi, Ghaith |
| contents | Simulation optimization (SO) is frequently challenged by noisy evaluations, high computational costs, and complex, multimodal search landscapes. This paper introduces Tabu-Enhanced Simulation Optimization (TESO), a novel metaheuristic framework integrating adaptive search with memory-based strategies. TESO leverages a short-term Tabu List to prevent cycling and encourage diversification, and a long-term Elite Memory to guide intensification by perturbing high-performing solutions. An aspiration criterion allows overriding tabu restrictions for exceptional candidates. This combination facilitates a dynamic balance between exploration and exploitation in stochastic environments. We demonstrate TESO's effectiveness and reliability using an queue optimization problem, showing improved performance compared to benchmarks and validating the contribution of its memory components. Source code and data are available at: https://github.com/bulentsoykan/TESO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems Soykan, Bulent Mondesire, Sean Rabadi, Ghaith Neural and Evolutionary Computing Artificial Intelligence Simulation optimization (SO) is frequently challenged by noisy evaluations, high computational costs, and complex, multimodal search landscapes. This paper introduces Tabu-Enhanced Simulation Optimization (TESO), a novel metaheuristic framework integrating adaptive search with memory-based strategies. TESO leverages a short-term Tabu List to prevent cycling and encourage diversification, and a long-term Elite Memory to guide intensification by perturbing high-performing solutions. An aspiration criterion allows overriding tabu restrictions for exceptional candidates. This combination facilitates a dynamic balance between exploration and exploitation in stochastic environments. We demonstrate TESO's effectiveness and reliability using an queue optimization problem, showing improved performance compared to benchmarks and validating the contribution of its memory components. Source code and data are available at: https://github.com/bulentsoykan/TESO. |
| title | TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2512.24007 |