针对噪声黑盒问题的TESO禁忌增强仿真优化算法

摘要

仿真优化(SO)常面临评估噪声、高计算成本及复杂多峰搜索空间的挑战。本文提出禁忌增强仿真优化(TESO)这一新型元启发式框架，将自适应搜索与基于记忆的策略相融合。TESO利用短期禁忌列表防止循环搜索并促进多样化，通过长期精英记忆库对优质解进行扰动以引导集中搜索。引入的渴望准则允许对特殊候选解突破禁忌限制。这种组合在随机环境中实现了探索与开发的动态平衡。我们以排队系统优化问题验证TESO的有效性与可靠性，结果表明其性能优于基准算法，并证实了记忆组件的贡献。源代码与数据详见：https://github.com/bulentsoykan/TESO。

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.

针对噪声黑盒问题的TESO禁忌增强仿真优化算法

TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems

摘要

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