Metaheuristic optimization of laminated composite structures: methods, trends, and future directions
Composite Structures, vol.393, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Review
- Volume: 393
- Publication Date: 2026
- Doi Number: 10.1016/j.compstruct.2026.120574
- Journal Name: Composite Structures
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chimica, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Keywords: Evolutionary algorithms, Genetic algorithm, Layup optimization, Metaheuristic algorithm, Optimization laminates
- Süleyman Demirel University Affiliated: Yes
Abstract
Laminated composite structures are widely used in aerospace, automotive, marine, and civil engineering due to their high strength-to-weight ratio, tailorability, and durability. However, their anisotropic nature, multiple interacting failure modes, and multi-physics loading conditions make optimal design a high-dimensional, nonlinear, and multi-objective challenge. Conventional gradient-based optimization often struggles in such complex design spaces, prompting the growing use of metaheuristic algorithms. This review goes beyond conventional algorithm-centric surveys by introducing a framework-level synthesis of metaheuristic optimization in laminated composites, explicitly integrating physics-informed modeling, uncertainty-aware design, and digital twin-enabled optimization paradigms, which are largely absent in existing reviews. Key trends identified include the superiority of hybrid and surrogate-assisted approaches in reducing computational cost, the role of adaptive operators in preventing premature convergence, and the potential of physics-informed and uncertainty-aware metaheuristics for real-world implementation. This work provides researchers and engineers with a comprehensive and comparative reference to guide the selection and development of metaheuristic algorithms. By synthesizing two decades of algorithmic progress, this review not only provides a comparative landscape but also codifies the fundamental challenges, such as the combinatorial curse and the simulation-to-reality gap, that define the current frontier, thereby establishing a clear roadmap for the next generation optimization frameworks.