Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations

• URL: http://arxiv.org/abs/2509.05445v1
• Date: Fri, 05 Sep 2025 18:55:33 GMT
• Title: Robustness and Invariance of Hybrid Metaheuristics under Objective Function Transformations
• Authors: Grzegorz Sroka, Sławomir T. Wierzchoń,
• Abstract summary: This paper evaluates the robustness and structural invariance of hybrid population-based metaheuristics under various objective space transformations.<n>A lightweight plug-and-play hybridization operator is applied to nineteen state-of-the-art algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper evaluates the robustness and structural invariance of hybrid population-based metaheuristics under various objective space transformations. A lightweight plug-and-play hybridization operator is applied to nineteen state-of-the-art algorithms-including differential evolution (DE), particle swarm optimization (PSO), and recent bio-inspired methods-without modifying their internal logic. Benchmarking on the CEC-2017 suite across four dimensions (10, 30, 50, 100) is performed under five transformation types: baseline, translation, scaling, rotation, and constant shift. Statistical comparisons based on Wilcoxon and Friedman tests, Bayesian dominance analysis, and convergence trajectory profiling consistently show that differential-based hybrids (e.g., hIMODE, hSHADE, hDMSSA) maintain high accuracy, stability, and invariance under all tested deformations. In contrast, classical algorithms-especially PSO- and HHO-based variants-exhibit significant performance degradation under non-separable or distorted landscapes. The findings confirm the superiority of adaptive, structurally resilient hybrids for real-world optimization tasks subject to domain-specific transformations.

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