Pedrammehr, Siamak, and Chalak Qazani, Mohammad Reza (2024) Special Issue “Optimisation Models and Applications”. Axioms, 13 (1). 45.

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Abstract

[Extract] Optimisation models have transcended their origins to become indispensable tools across many fields, including engineering, economics, the environment, health, systems of systems, businesses, and beyond. These models serve as guiding lights, illuminating the path toward optimal solutions. Within the optimisation realm, the following four distinct categories emerge, akin to constellations in the scientific cosmos: physics-based optimisation algorithms, swarm-based optimisation algorithms, game-based optimisation algorithms, and evolutionary-based optimisation algorithms. Over the past half-century, the optimisation domain has been a crucible where theory meets practice, generating solutions that have left indelible marks on diverse applications. In this Special Issue, our study takes us to the heart of the intricate interplay between optimisation models and their practical applications. In this realm, theory and reality merge to address fundamental scientific challenges. We earnestly invite researchers, akin to celestial navigators, to contribute their original, high-quality research papers. Let us explore the celestial expanse of optimisation and its transformative applications.

Research Statement

Research Background	Optimisation models have become essential across mathematics, computer science, engineering, economics, and health sciences. Over the past five decades, advances in mathematical optimisation, swarm intelligence, evolutionary computation, and control theory have enabled researchers to tackle increasingly complex and uncertain systems. Yet, challenges remain in ensuring efficiency, robustness, and scalability for real-world problems such as traffic networks, industrial design, biomedical engineering, and secure communications. This Special Issue situates itself within this evolving landscape, highlighting how theoretical frameworks can be transformed into practical methodologies across diverse domains.
Research Contribution	This Special Issue contributes by: Advancing Theory: introducing novel approaches in robust optimisation, duality theorems, fractional-order systems, and convex/nonconvex analysis. Innovating Algorithms: exploring swarm-based, evolutionary-based, and game-theoretic optimisation algorithms with improvements in accuracy, convergence, and stability. Delivering Applications: spanning traffic equilibrium modelling, chaotic system control, biomechanics of human motion, license plate recognition, and gear reverse engineering. Collectively, these works bridge mathematical rigour with practical impact, illustrating optimisation’s expanding role across science and technology.
Research Significance	The Special Issue underscores optimisation’s interdisciplinary significance in addressing challenges such as sustainable transport, efficient healthcare technologies, secure communication, and advanced manufacturing. By combining theory with application, it demonstrates optimisation’s potential to enhance efficiency, robustness, and resilience in complex, data-driven systems. The outcomes not only advance scientific understanding but also provide actionable insights for practitioners, strengthening the role of optimisation as a transformative enabler across multiple sectors.
Item ID:	86718
Item Type:	Article (Editorial)
ISSN:	2075-1680
Copyright Information:	© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited:	14 Oct 2025 03:48
FoR Codes:	49 MATHEMATICAL SCIENCES > 4903 Numerical and computational mathematics > 490304 Optimisation @ 100%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 70%
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