3066-280XISSN (Online)
Volume 5 , Issue 2 , PP: 110-124, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
S. K. Towfek 1 * , Mona Ahmed Yassen 2
Doi: https://doi.org/10.54216/MOR.050206
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.
Earthquake engineering , Genetic Algorithms , Article Swarm Optimization , Differential Evolution , Ant Colony Optimization , Grey Wolf Optimization
[1] Xiancheng Mei, Chuanqi Li, Zhen Cui, Qian Sheng, Jian Chen, and Shaojun Li. Application of metaheuristic optimization algorithms-based three strategies in predicting the energy absorption property of a novel aseismic concrete material. Soil Dynamics and Earthquake Engineering, 173:108085, October 2023.
[2] Haijun Yang, Yongzhong Xu, Gengxin Peng, Guiping Yu, Meng Chen, Wensheng Duan, Yongfeng Zhu, Yongfu Cui, and Xingjun Wang. Particle swarm optimization and its application to seismic inversion of igneous rocks. International Journal of Mining Science and Technology, 27(2):349–357, March 2017.
[3] Farnaz Ahadian, Gebrail Bekda¸s, Sinan Melih Nigdeli, Sanghun Kim, and Zong Woo Geem. Metaheuristics-Based Optimization of a Control System Consisting of Underground Tuned Mass Dampers and Base Isolators for Seismic Structures. GeoHazards, 6(1):5, March 2025. Number: 1 Publisher: Multidisciplinary Digital Publishing Institute.
[4] Behzad Eftekhar, Omid Rezaifar, and Mohammad Saeed Karimi. A new hybrid meta-heuristic algorithm for optimum performance-based seismic designs of moment-resisting frames. Engineering Optimization, 54(6):1023–1043, June 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/0305215X.2021.1907574.
[5] Bo Fu, Xinrui Liu, and Jin Chen. Machine Learning-Based Hybrid Optimization Method for Tuned Mass Damper Considering Seismic Soil-Structure Interaction. Journal of Earthquake Engineering, 28(12):3410–3431, September 2024. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/13632469.2024.2334824.
[6] SSRN Electronic Library.
[7] Mahir M. Hason, Alaa Hussein Al-Zuhairi, Ammar N. Hanoon, Ali A. Abdulhameed, Ahmed W. Al Zand, and Imad S. Abbood. Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques. Latin American Journal of Solids and Structures, 19:e447, June 2022. Publisher: Individual owner.
[8] Sara Mostofi, Ahmet Can Altuni¸sik, Hasan Basri Ba¸saga, Fatih Yesevi Okur, Zafer Yilmaz, Patrick Had- ˘ inata, Ertugrul Taciro ˘ glu, Banu Aslan, and Tu ˘ grul Sezdirmez. A Big Data-Enabled Decision Support ˘ Model for Post-Earthquake Damage Classification of RC Buildings: A Case Study on February 6, Kahramanmara¸s Doublet Earthquakes. Journal of Earthquake Engineering, pages 1–23. Publisher: Taylor & Francis.
[9] Yang Wang and Haotian Ma. Multi-objective stochastic optimization of tuned mass dampers under earthquake excitation considering soil–structure interaction. Journal of Asian Architecture and Building Engineering, 23(5):1596–1611, September 2024. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/13467581.2023.2270754.
[10] Foad Akbari, Jaber Valizadeh, and Ashkan Hafezalkotob. Robust cooperative planning of relief logistics operations under demand uncertainty: a case study on a possible earthquake in Tehran. International Journal of Systems Science: Operations & Logistics, 9(3):405–428, July 2022. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/23302674.2021.1914767.
[11] B. L. N. Kennett and M. S. Sambridge. Earthquake location — genetic algorithms for teleseisms. Physics of the Earth and Planetary Interiors, 75(1):103–110, December 1992.
[12] Giuseppe Quaranta, Giuseppe Carlo Marano, Rita Greco, and Giorgio Monti. Parametric identification of seismic isolators using differential evolution and particle swarm optimization. Applied Soft Computing, 22:458–464, September 2014.
[13] Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design.
[14] Anooshiravan Farshidianfar and Saeed Soheili. Ant colony optimization of tuned mass dampers for earthquake oscillations of high-rise structures including soil–structure interaction. Soil Dynamics and Earthquake Engineering, 51:14–22, August 2013.
[15] A. Kaveh and V. R. Mahdavi. Shape optimization of arch dams under earthquake loading using metaheuristic algorithms. KSCE Journal of Civil Engineering, 17(7):1690–1699, November 2013.
[16] Gebrail Bekda¸s and Sinan Melih Nigdeli. Metaheuristic based optimization of tuned mass dampers under earthquake excitation by considering soil-structure interaction. Soil Dynamics and Earthquake Engineering, 92:443–461, January 2017.
[17] Ali Erdem Çerçevik, Özgür Av¸sar, and Oguzhan Hasançebi. Optimum design of seismic isolation sys- ˘ tems using metaheuristic search methods. Soil Dynamics and Earthquake Engineering, 131:106012, April 2020.
[18] Fabio Di Trapani, Antonio P. Sberna, and Giuseppe C. Marano. A novel genetic algorithm-based optimization framework for minimizing seismic retrofitting interventions costs in existing masonry structures. Procedia Structural Integrity, 44:1696–1703, January 2023.
[19] Jui-Sheng Chou and Julian Pratama Putra Thedja. Metaheuristic optimization within machine learningbased classification system for early warnings related to geotechnical problems. Automation in Construction, 68:65–80, August 2016.
[20] Mahir M. Hason, Alaa Hussein Al-Zuhairi, Ammar N. Hanoon, Ali A. Abdulhameed, Ahmed W. Al Zand, and Imad S. Abbood. Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques. Latin American Journal of Solids and Structures, 19:e447, June 2022. Publisher: Individual owner.
[21] Serdar Ulusoy, Sinan Melih Nigdeli, and Gebrail Bekda¸s. Novel metaheuristic-based tuning of PID controllers for seismic structures and verification of robustness. Journal of Building Engineering, 33:101647, January 2021.
[22] Huan Luo and Stephanie German Paal. Metaheuristic least squares support vector machine-based lateral strength modelling of reinforced concrete columns subjected to earthquake loads. Structures, 33:748– 758, October 2021.
[23] Ali Tanoumand, Mohammadreza Mashayekhi, Ali Majdi, and Ehsan Noroozinejad Farsangi. A metaheuristic-based approach for optimizing the allocation of emergency water reservoirs for fire following earthquake suppression. Results in Engineering, 27:105925, September 2025.
[24] A. Keswani, R. Patel, and V. Singh. Combined hsmpso–sca hybrid algorithm for optimal load dispatch in power systems. International Journal of Electrical Power & Energy Systems, 145:107880, 2023.
[25] A new hybrid meta-heuristic algorithm for optimum performance-based seismic designs of momentresisting frames: Engineering Optimization: Vol 54 , No 6 - Get Access.
[26] Muhammed Ulucan, Emrullah Gunduzalp, Güngör Yildirim, Bilal Alatas, and Kursat Esat Alyamac. Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization-based metaheuristic approach to neural network modeling for circular economy and disaster resilience. Structural Concrete, 26(2):1226–1244, 2025. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/suco.202400070.
[27] M. Tayab, Z. Shah, and N. Ahmed. Ensemble modeling for solar pv power forecasting with tunicate swarm algorithm and whale optimization algorithm. Solar Energy, 236:143–159, 2022.
[28] S. Thota, P. Rao, and S. Mehta. A novel coati optimization algorithm integrated with support vector regression for building energy consumption forecasting. Energy and Buildings, 290:112691, 2024.
[29] B. Pudasaini and S. M. Shahandashti. Seismic Resilience Enhancement of Water Pipe Networks Using Hybrid Metaheuristic Optimization. pages 428–436, August 2020. Publisher: American Society of Civil Engineers.
[30] Maliheh Khorsi, S. Kamal Chaharsooghi, Ali Husseinzadeh Kashan, and Ali Bozorgi-Amiri. A dynamic robust optimization model for the routing-scheduling problem in humanitarian logistics and its solution via grouping metaheuristic algorithm (case study: Tehran earthquake). Quarterly Journal of Transportation Engineering, 12(4):833–853, June 2021. Publisher: Parseh Designers Transportation Research Institute.
[31] Mahdi Azizi and Siamak Talatahari. Improved arithmetic optimization algorithm for design optimization of fuzzy controllers in steel building structures with nonlinear behavior considering near fault ground motion effects. Artificial Intelligence Review, 55(5):4041–4075, June 2022.
[32] Jui-Sheng Chou, Chi-Yun Liu, Handy Prayogo, Riqi Radian Khasani, Danny Gho, and Gretel Gaby Lalitan. Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristicsoptimized machine learning. Journal of Building Engineering, 61:105046, December 2022.
[33] Mohammad Al-Rawashdeh, Moh’d Al Nawaiseh, Isam Yousef, Majdi Bisharah, Sajeda Alkhadrawi, and Hamza Al-Bdour. Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms. Asian Journal of Civil Engineering, 25(1):253–264, January 2024.
[34] Jie Cai, Chunbo Fang, Juncheng Li, Mingang Li, Sheng Chen, and Jinwen Zhou. Seismic hazard prediction in mining activities using metaheuristic algorithms. Earth Science Informatics, 18(2):238, February 2025.
[35] Bikash Behera, Aloke Kumar Datta, and Apurba Pal. Earthquake resistant design of framed reinforced concrete building using artificial intelligence model. Asian Journal of Civil Engineering, 25(5):4329– 4342, July 2024.
