Metaheuristic Optimization in Structural Engineering Design and Analysis: A Survey

Authors

https://doi.org/10.48313/maa.vi.64

Abstract

This paper presents a comprehensive review of metaheuristic algorithms across engineering disciplines over the period 2000-2025. We systematically analyze the literature on metaheuristic algorithms and their applications to structural design, power systems, control, manufacturing, aerospace, p. The review covers approximately 192 papers, providing a structured taxonomy of algorithms, applications, and evaluation methodologies. We identify key trends including the shift toward hybrid approaches, integration of Machine Learning (ML), and growing emphasis on explainability. The survey reveals that constraint handling, multi-objectivity, real-time, scalability, robust remain significant open problems. We provide detailed analysis of evaluation protocols, benchmark suites, and statistical methodologies. Future research directions include hybrid algorithm design, quantum-inspired methods, and standardized benchmarking frameworks.

Keywords:

Metaheuristic optimization, Engineering, Review, Survey, Structural design, Power systems, Contro

References

  1. [1] Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog. https://gwern.net/doc/reinforcement-learning/exploration/1973-rechenberg.pdf
  2. [2] Schwefel, H. P. (1975). Evolutionsstrategie und numerische optimierung [Thesis]. https://doi.org/10.1007/978-3-0348-5927-1_5
  3. [3] Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. MIT Press. https://mitpress.mit.edu/9780262581110/adaptation-in-natural-and-artificial-systems/
  4. [4] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
  5. [5] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (cybernetics), 26(1), 29–41. https://doi.org/10.1109/3477.484436
  6. [6] Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. John Wiley & Sons. https://doi.org/10.1002/bs.3830110403
  7. [7] Kirkpatrick, S. (1983). Improvement of reliabilities of regulations using a hierarchical structure in a genetic network. Science, 220, 671–680. https://doi.org/10.1126/science.220.4598.671
  8. [8] Glover, F. (1989). Tabu search—part I. ORSA journal on computing, 1(3), 190–206. https://doi.org/10.1287/ijoc.1.3.190
  9. [9] Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
  10. [10] Hansen, N. (2006). The CMA evolution strategy: A comparing review. In Towards a new evolutionary computation: Advances on estimation of distribution algorithms (pp. 75–102). Springer. https://doi.org/10.1007/3-540-32494-1_4
  11. [11] Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. https://abc.erciyes.edu.tr/pub/tr06_2005.pdf
  12. [12] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61 (In Persian). https://doi.org/10.1016/j.advengsoft.2013.12.007
  13. [13] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  14. [14] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris Hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028.
  15. [15] Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
  16. [16] Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International journal of bio-inspired computation, 2(2), 78–84. https://doi.org/10.1504/IJBIC.2010.032124

Downloads

Published

2024-06-17

Issue

Vol. 1 No. 2 (2024): Metaheuristic Algorithms with Applications

Section

Articles

Categories

How to Cite

Janardan, B. ., & Swain, K. . (2024). Metaheuristic Optimization in Structural Engineering Design and Analysis: A Survey. Metaheuristic Algorithms With Applications, 1(2), 171-180. https://doi.org/10.48313/maa.vi.64

Similar Articles

1-10 of 32

You may also start an advanced similarity search for this article.