TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
This work addresses optimization challenges in stochastic simulations, which is incremental as it builds on existing metaheuristics with memory enhancements.
The paper tackles simulation optimization in noisy, high-cost environments by introducing TESO, a metaheuristic that integrates adaptive search with memory-based strategies, demonstrating improved performance in a queue optimization problem compared to benchmarks.
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.