针对噪声干扰的复杂优化问题，提出记忆增强型搜索框架TESO。
TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
- 结合短期禁忌表防循环，长期精英记忆引导优化方向
- 在排队系统优化中性能优于基准方法，收敛更快更稳定
- 适合高噪声、多峰优化场景，尤其适合工程仿真类问题
仿真优化常面临评估噪声大、计算成本高和复杂多峰搜索空间的挑战。本文提出一种新型元启发式框架——禁忌增强仿真优化（TESO），融合自适应搜索与基于记忆的策略。通过短期禁忌表防止搜索循环，促进多样化；长期精英记忆通过扰动高性能解来引导强化。同时引入期望准则，允许对表现极佳的候选解突破禁忌限制。该设计在随机环境中实现探索与利用的动态平衡。我们在一个排队系统优化问题上验证了TESO的有效性与可靠性，结果表明其性能优于多个基准方法，并证实了记忆组件的关键贡献。源代码与数据见：https://github.com/bulentsoykan/TESO。
原文摘要 · Abstract (English)
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.
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