TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
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.
6957e7af832867f2535259cb
Bulent Soykan
claimed_verified
2026-02-09T08:36:59.416Z
626273fbcbebf7e1ac2820ab
https://cdn-avatars.huggingface.co/v1/production/uploads/626273fbcbebf7e1ac2820ab/VPc5BVvdSel6ox7yzrcvb.jpeg
Bulent Soykan
bulentsoykan
user
bulentsoykan
6957e7af832867f2535259cc
Sean Mondesire
6957e7af832867f2535259cd
Ghaith Rabadi
TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
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.