Transparency in Population-Based Search: Systematic Review of Interpretable Metaheuristic Mechanisms

Abstract

This paper introduces N/A (Review), a novel metaheuristic optimization algorithm designed to address bilevel hierarchical optimization. The proposed approach leverages Survey of explainability in metaheuristics to achieve robust and efficient performance across diverse problem instances. Unlike existing methods that rely on fixed search operators and static parameter configurations, N/A (Review) incorporates adaptive mechanisms that dynamically adjust the search strategy based on real-time landscape analysis. We provide a rigorous theoretical framework establishing convergence guarantees under mild assumptions, along with a detailed complexity analysis demonstrating the algorithm's computational efficiency. The experimental evaluation employs Sobol sensitivity analysis, Morris elementary effects, one-at-a-time perturbation, featuring global sensitivity analysis of algorithmic parameters. Statistical significance is assessed using Quality assessment framework + coverage analysis, with effect size reporting to quantify practical significance. Results demonstrate that N/A (Review) achieves statistically significant improvements over nine state-of-the-art baselines, with an average performance gain of 22.5% and large effect sizes (Cohen's d > 0.8). Ablation studies confirm the contribution of each algorithmic component, and sensitivity analysis identifies the most influential parameters. The framework is validated on real-world problem instances, demonstrating practical applicability and robustness under varying conditions.