The increasing frequency and severity of seismic events worldwide demand innovative and adaptive solutions in earthquake engineering, early warning, and emergency response systems. Traditional deterministic optimization techniques often fall short in addressing the high-dimensional, nonlinear, and data-uncertain nature of many seismic problems. In contrast, metaheuristic algorithms—stochastic, population-based search methods inspired by natural phenomena—have emerged as powerful alternatives capable of providing robust and near-optimal solutions in complex environments. This review synthesizes the growing body of research on the application of metaheuristic optimization techniques across diverse earthquake-related domains. We examine over fifty influential studies that employ algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), and modern hybrid and multi-objective approaches. Applications span a wide spectrum—from seismic source localization and structural design, to tuned mass damper configuration, sensor placement, earthquake classification, and real-time emergency resource allocation. The review identifies key trends, including the evolution from single-algorithm methods to hybrid models that combine the strengths of multiple metaheuristics, and the transition from static to dynamic, real-time optimization frameworks. Addi-tionally, the integration of machine learning and reinforcement learning with metaheuristic search is shown to significantly improve the adaptability, accuracy, and performance of seismic systems. For instance, PSO-optimized neural networks and GA-tuned support vector machines have demonstrated enhanced precision in peak ground acceleration prediction and seismic zone classification. Despite their advantages, metaheuristic techniques face several open challenges. These include scalability to large-scale problems, lack of standard benchmarks and datasets, computational expense in high-fidelity simulations, and limited transparency in multi-stage or learning-augmented models. Moreover, reproducibility and generalizability of results remain underdeveloped due to inconsistent reporting standards and proprietary data. This review highlights the need for community-driven initiatives to establish open datasets, reproducible benchmarking platforms, and standardized performance metrics. Future directions emphasize lightweight, adaptive algorithms capable of operating in real-time environments, as well as interpretable and sustainable optimization frameworks suit-able for deployment on embedded systems and edge devices. In summary, metaheuristic optimization holds immense promise for advancing earthquake resilience. Its continued development—through hybridization, integration with AI, and emphasis on transparency and real-world applicability—will be instrumental in shaping the next generation of intelligent seismic risk mitigation tools.
link
https://doi.org/10.54216/MOR.050206