# TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems

arXiv: https://arxiv.org/abs/2512.24007 · HTML: https://mlsanity.com/paper/2512.24007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Review on Ranking and Selection: A New Perspective](https://mlsanity.com/paper/2008.00249/) (2020)
- [Simulation optimization: A review of algorithms and applications](https://mlsanity.com/paper/1706.08591/) (2017)
- [Surrogate-Based Simulation Optimization](https://mlsanity.com/paper/2105.03893/) (2021)
- [Review of Large-Scale Simulation Optimization](https://mlsanity.com/paper/2403.15669/) (2024)
- [Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach](https://mlsanity.com/paper/2310.07970/) (2023)

## Built on by (most cited first, 1 of 1)

- [Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling](https://mlsanity.com/paper/2602.08052/) (2026)

## Abstract (arXiv)

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.