Research Article

Constraint-Aware Entropy-Adaptive Swarm Search for Civil Infrastructure Sizing: Formulation and Reported Benchmark Evidence

Authors

Khan Hassan M. * Department of Civil Engineering, NED University of Engineering & Technology, Karachi, Pakistan.
Ahmed M. El-Sayed Department of Structural Engineering, Ain Shams University, Cairo, Egypt.
Rania Boutros Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand.
Tomasz Lewandowski Department of Civil Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland.

https://doi.org/10.22105/opt.v3i3.119

Downloads

Abstract

SI-CASS is a proposed population-search framework for civil sizing that combines an operator portfolio, structural-response-based repair and entropy-triggered reinjection. The formulation distinguishes simplified optimization constraints from the complete checks required by a project design code. Repair is a local heuristic followed by structural reevaluation; neither catalog rounding nor area scaling guarantees feasibility or code certification. A conditional coverage argument describes requirements for an infinite search extension, while the specified finite-budget process has no established global convergence rate. Reported summaries from six problems and 30 runs retain the original numerical observations, including a 25-bar-truss mean of 247.1 versus 252.4 for SHADE and 263.5 for PSO. The underlying models, units, implementation and run-level outputs are unavailable, and inconsistencies among the tables, statistical adjustments and figures prevent verification of significance and causal component effects. The contribution is the stated search architecture and an explicit account of the evidence needed to assess it. The reported designs are not certified engineering deliverables.

Keywords

Structural sizing Adaptive operator selection Constraint handling Population entropy Evaluation accounting Reproducibility

References (22)

[1] Avcı, M. S., Yavuz, D., Ercan, E., & Nuhoğlu, A. (2024). Efficient sizing and layout optimization of truss benchmark structures using ISRES algorithm. Applied sciences, 14(8), 3324. https://doi.org/10.3390/app14083324

[2] Mehta, P., Tejani, G. G., & Mousavirad, S. J. (2025). Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm. Scientific reports, 15(1), 14575. https://doi.org/10.1038/s41598-025-97133-w

[3] Tanabe, R., & Fukunaga, A. (2013). Success-history based parameter adaptation for differential evolution. 2013 ieee congress on evolutionary computation (pp. 71–78). IEEE. https://doi.org/10.1109/CEC.2013.6557555

[4] Hansen, N. (2006). The CMA Evolution Strategy: A Comparing Review. In Towards a new evolutionary computation: advances in the estimation of distribution algorithms (pp. 75–102). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-32494-1_4

[5] 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

[6] Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968

[7] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

[8] Yang, X. S., & Deb, S. (2009). Cuckoo search via lévy flights. 2009 world congress on nature & biologically inspired computing (NABIC) (pp. 210–214). IEEE. https://doi.org/10.1109/NABIC.2009.5393690

[9] Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer methods in applied mechanics and engineering, 186(2), 311–338. https://doi.org/10.1016/S0045-7825(99)00389-8

[10] Takahama, T., & Sakai, S. (2010). Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation. IEEE congress on evolutionary computation (pp. 1–9). IEEE. https://doi.org/10.1109/CEC.2010.5586484

[11] Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11), 1245–1287. https://doi.org/10.1016/S0045-7825(01)00323-1

[12] Papadrakakis, M., Lagaros, N. D., & Tsompanakis, Y. (1998). Structural optimization using evolution strategies and neural networks. Computer methods in applied mechanics and engineering, 156(1), 309–333. https://doi.org/10.1016/S0045-7825(97)00215-6

[13] Ton-That, H. L. (2026). Identification of damage in planar truss structures within a multiphysics framework using metaheuristic optimization algorithms. Journal of complex and multiphysics engineering systems, 1(2), 138–147. https://doi.org/10.56578/jcmes010202

[14] American Institute of Steel Construction. (2005). Specification for structural steel buildings. https://www.aisc.org/aisc/publications/historic-standards/specification-for-structural-steel-buildings/

[15] European Committee for Standardization. (2005). Eurocode 3: Design of steel structures—part 1-1: General rules and rules for buildings. https://eurocodes.jrc.ec.europa.eu/EN-Eurocodes/eurocode-3-design-steel-structures

[16] European Committee for Standardization. (2002). Eurocode: Basis of structural design. https://www.bing.com/ck/a?!&&p=97c394ed83be2e71a333bea7a1548f199eca3b5a90da0d06c4a1eb7b9f4e83

[17] American Concrete Institute. (2019). Building code requirements for structural concrete (ACI 318-19) and commentary (ACI 318R-19). https://www.concrete.org/Portals/0/Files/PDF/Previews/318-19_preview.pdf

[18] Shokouhifar, M., & Goli, A. (2023). Designing a resilient--sustainable supply chain network of age-differentiated blood platelets using vertical--horizontal transshipment and grey wolf optimizer. International journal of environmental research and public health, 20(5), 4078. https://doi.org/10.3390/ijerph20054078

[19] Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press. https://d1wqtxts1xzle7.cloudfront.net/98698869/1216504-libre.pdf?1676455734

[20] Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The journal of machine learning research, 7, 1–30. https://doi.org/10.5555/1248547.1248548

[21] Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and evolutionary computation, 1(1), 3–18. https://doi.org/10.1016/j.swevo.2011.02.002

[22] Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2), 161–174. https://doi.org/10.1080/00401706.1991.10484804

Article Metrics

Published 2026-09-19
Issue Vol. 3 No. 3 (2026): Optimality
Section Articles
Document Type Research Article
Views 0
Downloads 0

License

Copyright (c) 2026 Optimality

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

View License Details

How to Cite

Hassan M. , K., El-Sayed, A. M. ., Boutros, R. ., & Lewandowski, T. . (2026). Constraint-Aware Entropy-Adaptive Swarm Search for Civil Infrastructure Sizing: Formulation and Reported Benchmark Evidence. Optimality, 3(3), 270-282. https://doi.org/10.22105/opt.v3i3.119

Download Citation

Similar Articles

Mei-Lin Nakamura, Pavel D. Sokolov, Adaeze O. Okonkwo, Adaptive Operator Portfolio Search with Fold Based Landscape Probing , Optimality: Vol. 3 No. 3 (2026): Optimality
Tanvir Siraj, Rashedul Haque, Saimon Chowdhury, Nazrul Islam, Basudeb Biswas, Kamrul Hasan Chowdhury, Analyzing Challenges in Enterprise Resource Planning (ERP) Implementation in a Safety Inspection Company: an IVT2IF DEMATEL Approach , Optimality: Vol. 1 No. 2 (2024): OPT
Bilal Ahmad Khan, Wolfgang H. Müller, Distributed Influence Search for Engineering Optimization: Conditional Analysis and Reported Benchmarks , Optimality: Vol. 3 No. 3 (2026): Optimality
Phi-Hung Nguyen, Lan-Anh Thi Nguyen, Hong-Quan Le, Tra-Giang Vu, Thu-Hoai Thi Nguyen, Huong-Giang Le Hoang, Towards Sustainable Tourism: Utilizing Fuzzy AHP-TOPSIS Approach for Green Hotel Selection , Optimality: Vol. 3 No. 2 (2026): Optimality
Osawe Osayande, Raphael Sylvester Ebhojiaye, Ekom Mike Etuk, Computational Analysis of Flow Dynamics and Angular Inclination Effects on Single-Axis Solar Tracking Systems for Renewable Energy Applications , Optimality: Vol. 3 No. 2 (2026): Optimality
Snigdharani Panda, Pritiprava Mishra, Application of GenAI in Outcome-Based Education in Teaching Learning Processes , Optimality: Vol. 3 No. 1 (2026): OPT
Bahram Farhadi Nia, Mohammad Reza Ahangari, Seyyed Javad Ebrahimi, Enhancing Generative Adversarial Networks with Structural Similarity Index and Fuzzy Logic-Based Loss Functions (SSIM_T) , Optimality: Vol. 2 No. 2 (2025): OPT
Trayambak Rai, Optimizing IoT Device Communication: Adaptive Load Balancing and Data Prioritization for Efficient Cloud and Edge Integration , Optimality: Vol. 1 No. 2 (2024): OPT
Hamiden Abd El-Wahed Khalifa, Darko Bozanic, Hashem Saberi Najafi, Pavan Kumar, Solving Vendor Selection Problem by Interval Approximation of Piecewise Quadratic Fuzzy Number , Optimality: Vol. 1 No. 1 (2024): OPT
Seyyed Farid Ghannadpour, Daniel A. Ferreira, Adaptive Evolutionary Search in Changing Environments: Moving Peaks and a Two-Species Chemostat Surrogate , Optimality: Vol. 3 No. 3 (2026): Optimality

1-10 of 26 Next

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