TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems

TL;DR

TESO is a new metaheuristic framework that enhances simulation optimization for noisy black box problems by integrating adaptive search with memory strategies, improving performance in complex stochastic landscapes.

Contribution

Introduces TESO, a novel Tabu-Enhanced Simulation Optimization framework combining adaptive search with memory-based strategies for better noisy black box problem solving.

Findings

TESO outperforms benchmark algorithms in queue optimization tasks.

Memory components significantly improve search effectiveness.

Demonstrates robustness in stochastic environments.

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.