From Heuristics to Hyperheuristics: Comparative Study and Real-World Impact in Optimization

Authors

  • Esraa Aljubarah * Independent Researcher, Nabels, Palestine.

https://doi.org/10.48313/maa.v2i2.39

Abstract

Optimization underpins decision-making across scientific, engineering, and data-driven domains. This paper presents a systematic, multi-layered comparison of heuristic, metaheuristic, and hyperheuristic algorithms, dissecting their architectural paradigms, convergence behaviors, parameter sensitivities, and scalability profiles. Through an exhaustive literature synthesis spanning 150+ peer-reviewed sources (2015–2025), implementation of 12 representative algorithms, and rigorous benchmarking on 8 standard test suites and 6 real-world datasets, we quantify performance across solution quality, computational efficiency, robustness, and generalization.Key contributions include: 1) A taxonomic framework unifying heuristic, metaheuristics, and hyperheuristics under a unified abstraction hierarchy, 2) Novel performance landscapes via Multi Dimensional Scaling (MDS) and Pareto frontier analysis, 3) Real-world impact assessment in logistics, manufacturing, healthcare, and smart infrastructure, and 4) Identification of emerging research vectors, including explainable hyperheuristics, transfer learning in algorithm selection, and integration with foundation models. The study concludes that while heuristics dominate in low-latency, interpretable settings, metaheuristics lead in robust global search, and hyperheuristics emerge as the automated, adaptive backbone for next-generation optimization systems.

Keywords:

Optimization, Algorithm taxonomy, Convergence analysis, Multi-dimensional scaling, Benchmarking

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Published

2025-06-15

Issue

Vol. 2 No. 2 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Aljubarah, E. . (2025). From Heuristics to Hyperheuristics: Comparative Study and Real-World Impact in Optimization. Metaheuristic Algorithms With Applications, 2(2), 134-151. https://doi.org/10.48313/maa.v2i2.39

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