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Optimization of Metallic Structures by Applying Genetic Algorithm

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Sustainability and Automation in Smart Constructions

Abstract

Computer-aided design is becoming a reality today through the significant development of computing tools. These calculation codes are often intended for an advanced project design phase. On the other hand, there are very few tools for early design assistance or pre-project design. Therefore, in this research, a methodology is proposed for solving the problem of the global design of a simple metal structure based on the genetic algorithms approach using Matlab. A comparative study is made on a 2D metal gantry using the profiles available in the Algerian market, namely IPE—IPN and HEA—HEB so as to have an optimal dimensioning in the inelastic field.

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References

  • Azad, M. A. K., Rocha, A. M. A., & Fernandes, E. M. (2015). Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm. Journal of Mathematical Modelling and Algorithms in Operations Research, 14(3), 313–330.

    Article  MathSciNet  Google Scholar 

  • Benanane, A. (2007, December). Réalization of a multi-function optimization platform applied to metal constructions Benanane Abdelkader These in order to obtain the state doctoral degree. Faculty of Architecture and Civil engineering, University of Science and Technology. Algeria c Department of Civil Engineering.

    Google Scholar 

  • Chantrelle, F. B., et al. (2011). Development of a multicriteria tool for optimizing the renovation of buildings. Applied Energy, 88(4), 1386–1394.

    Article  Google Scholar 

  • Crawford, B., Soto, R., Berr´ıos, N., Johnson, F., Paredes, F., Castro, C., & Norero, E. (2015). A binary cat swarm optimization algorithm for the non unicost set covering problem. Mathematical Problems in Engineering, 2015.

    Google Scholar 

  • De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems. Ph.D. dissertation, University of Michigan, Ann Arbor, MI.

    Google Scholar 

  • Eisenhower, B., et al. (2012). A methodology for meta-model based optimization in building energy models. Energy and Buildings, 47, 292–301.

    Article  Google Scholar 

  • Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. John Wiley & Sons.

    Google Scholar 

  • Fulcher, J. (2008). Computational intelligence: An introduction. In Computational intelligence: A compendium (pp. 3–78). Springer.

    Google Scholar 

  • Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89–98.

    Article  MathSciNet  Google Scholar 

  • Ghojogh, B., Sharifian, S., & Mohammad Zade, H. (2018). Tree-based optimization: A meta-algorithm for metaheuristic optimization.

    Google Scholar 

  • Hamdy, M., Hasan, A., & Siren, K. (2011). Impact of adaptive thermal comfort criteria on building energy use and cooling equipment size using a multi-objective optimization scheme. Energy and Buildings, 43(9), 2055–2067.26.

    Google Scholar 

  • Hamdy, M., Palonen, M., & Hasan, A. (2012). Implementation of pareto-archive NSGA-II algorithms to a nearly-zero-energy building optimisation problem. In J. Wright & M. Cook (Eds.), Proceedings of the 2012 building simulation and optimization conference (pp. 181–188). Loughborough, Leicestershire: Loughborough University. ISBN 978-1-897911-42-6.

    Google Scholar 

  • Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175–184.

    Article  MathSciNet  Google Scholar 

  • Holland, J., & Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Massachusetts: Addison-Wesley.

    Google Scholar 

  • Hopfe, C. J., et al. (2013). Robust multi-criteria design optimisation in building design. In J. Wright & M. Cook (Eds.), Proceedings of the 2012 building simulation and optimization conference (pp. 118–125). Loughborough University. ISBN 978-1-897911-42-6.

    Google Scholar 

  • Jin, Q., & Overend, M. (2012). Facade renovation for a public building based on a whole-life value approach. In J. Wright & M. Cook (Eds.), Proceedings of the 2012 building simulation and optimization conference (pp. 378–385). Loughborough, Leicestershire: Loughborough University.

    Google Scholar 

  • John, H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. Cambridge, MA: MIT Press.

    Google Scholar 

  • Koza, J. R. (1992). Genetic programming: On the programming of computers by natural selection. MIT Press.

    Google Scholar 

  • Lee, J. H., & Lee, J. H. (2007). Optimization of indoor climate conditioning with passive and active methods using GA and CFD. Building and Environment, 42(9), 3333–3340.

    Google Scholar 

  • Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.

    Article  Google Scholar 

  • Mirjalili, S., Mirjalili, S. M., & Yang, X.-S. (2014a). Binary bat algorithm. Neural Computing and Applications, 25(3–4), 663–681.

    Article  Google Scholar 

  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014b). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  • Nguyen, A. T. (2013). Sustainable housing in Vietnam: Climate responsive design strategies to optimize thermalcomfort. PhD thesis. Universite de Liege.

    Google Scholar 

  • Roy, R., Hinduja, S., & Teti, R. (2008). Recent advances in engineering design optimisation: Challenges and future trends. CIRP Annals—Manufacturing Technology, 57(2), 697–715.

    Article  Google Scholar 

  • Sahab, M. G., Toropov, V. V., & Gandomi, A. H. (2013). A review on traditional and modern structural optimization: problems and techniques. In A. H. Gandomi et al. (Eds.), Metaheuristic applications in structures and infrastructures (pp. 25–47). Oxford: Elsevier. ISBN 9780123983640.

    Google Scholar 

  • Talbi, E. -G. (2009). Metaheuristics: From design to implementation, vol. 74. John Wiley & Sons.

    Google Scholar 

  • Uymaz, S. A., Tezel, G., & Yel, E. (2015). Artificial algae algorithm (aaa) for nonlinear global optimization. Applied Soft Computing, 31, 153–171.

    Article  Google Scholar 

  • Yang, X. -S. (2010). Nature-inspired metaheuristic algorithms. Luniver Press.

    Google Scholar 

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Acknowledgments

The authors are thankful to the Prof. Cherif Zine-El-Abiddine, University of Tlemcen, for his help in this work.

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Correspondence to Mohammed Issam Eddine Terki Hassaine .

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Terki Hassaine, M.I.E., Bourdim, S.M., Benanane, A., Zelmat, Y. (2021). Optimization of Metallic Structures by Applying Genetic Algorithm. In: Rodrigues, H., Gaspar, F., Fernandes, P., Mateus, A. (eds) Sustainability and Automation in Smart Constructions. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-35533-3_41

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