TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
Bulent Soykan, Sean Mondesire, Ghaith Rabadi
2026-01-02
Summary
This paper introduces a new method called Tabu-Enhanced Simulation Optimization, or TESO, which is designed to find the best solutions to complex problems when it's difficult to get clear answers or when each possible solution takes a long time to evaluate.
What's the problem?
Many real-world problems are hard to solve because figuring out how good a solution is can be unreliable, takes a lot of computing power, and have many different possible good solutions scattered around. Traditional methods often get stuck finding only *a* good solution, but not necessarily the *best* one, or they waste time revisiting solutions they've already tried.
What's the solution?
TESO tackles this by combining two key ideas. First, it uses a 'Tabu List' which is like a short-term memory that prevents the method from immediately going back to solutions it just explored, encouraging it to try new things. Second, it uses an 'Elite Memory' which remembers the best solutions found so far and uses them as a starting point for further improvement. A special rule allows the method to sometimes ignore the Tabu List if it finds a really promising solution. This balance between remembering good solutions and exploring new possibilities helps TESO navigate these difficult problems effectively.
Why it matters?
This research is important because it provides a more reliable and efficient way to optimize complex systems, like queuing systems. By improving the search process, TESO can lead to better decisions and outcomes in areas where finding the best solution is crucial, and the publicly available code allows others to build upon and test this new approach.
Abstract
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.