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research-article

Variable functioning and its application to large scale steel frame design optimization

Published: 27 December 2022 Publication History

Abstract

To solve complex real-world problems, heuristics and concept-based approaches can be used to incorporate information into the problem. In this study, a concept-based approach called variable functioning (Fx) is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subsets of variables are defined with functions using information prior to optimization; thus, the function variables are optimized instead of modifying the variables in the search process. By using the problem structure analysis technique and engineering expert knowledge, the Fx method is used to enhance the steel frame design optimization process as a complex real-world problem. Herein, the proposed approach was coupled with particle swarm optimization and differential evolution algorithms then applied for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that Fx can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding.

References

[1]
Audoux Y, Montemurro M, and Pailhes J Non-uniform rational basis spline hyper-surfaces for metamodelling Comput Methods Appl Mech Eng 2020 364 112918
[2]
Azad SK Design optimization of real-size steel frames using monitored convergence curve Struct Multidisc Optim 2021 63 1 267-288
[3]
Azad SK and Hasançebi O Computationally efficient discrete sizing of steel frames via guided stochastic search heuristic Comput Struct 2015 156 12-28
[4]
Becerra RL and Coello CAC Cultured differential evolution for constrained optimization Comput Methods Appl Mech Eng 2006 195 33–36 4303-4322
[5]
Bigham A and Gholizadeh S Topology optimization of nonlinear single-layer domes by an improved electro-search algorithm and its performance analysis using statistical tests Struct Multidisc Optim 2020 62 4 1821-1848
[6]
Camp CV and Assadollahi A Co 2 and cost optimization of reinforced concrete footings using a hybrid big bang-big crunch algorithm Struct Multidisc Optim 2013 48 2 411-426
[7]
Chen W, Weise T, Yang Z, Tang K (2010) Large-scale global optimization using cooperative coevolution with variable interaction learning. In: International conference on parallel problem solving from nature, Springer, pp 300–309
[8]
Costa G, Montemurro M, and Pailhès J A general hybrid optimization strategy for curve fitting in the non-uniform rational basis spline framework J Optim Theory Appl 2018 176 1 225-251
[9]
Davison JH and Adams PF Stability of braced and unbraced frames J Struct Div 1974 100 2 319-334
[10]
Deb K An efficient constraint handling method for genetic algorithms Comput Methods Appl Mech Eng 2000 186 2–4 311-338
[11]
Deb K and Myburgh C A population-based fast algorithm for a billion-dimensional resource allocation problem with integer variables Eur J Oper Res 2017 261 2 460-474
[12]
Eiben AE and Smith J From evolutionary computation to the evolution of things Nature 2015 521 7553 476
[13]
Gandomi AH and Yang XS Benchmark problems in structural optimization Computational optimization, methods and algorithms 2011 New York Springer 259-281
[14]
Ghasemi MR and Farshchin M Ant colony optimisation-based multiobjective frame design under seismic conditions Proc Inst Civ Eng-Struct Build 2011 164 6 421-432
[15]
Gholizadeh S and Poorhoseini H Seismic layout optimization of steel braced frames by an improved dolphin echolocation algorithm Struct Multidisc Optim 2016 54 4 1011-1029
[16]
Hasançebi O, Çarbaş S, Doğan E, Erdal F, and Saka M Comparison of non-deterministic search techniques in the optimum design of real size steel frames Comput struct 2010 88 17–18 1033-1048
[17]
De Jong K Learning with genetic algorithms: an overview Mach Learn 1988 3 2–3 121-138
[18]
Juliani MA and Gomes WJ An efficient Kriging-based framework for computationally demanding constrained structural optimization problems Struct Multidisc Optim 2022 65 1 1-16
[19]
Kennedy R (1995) J. and eberhart, particle swarm optimization. In: Proceedings of IEEE international conference on neural networks IV, pages, vol 1000
[20]
Lamberti L and Pappalettere C Metaheuristic design optimization of skeletal structures: a review Comput Technol Rev 2011 4 1 1-32
[21]
Liu J, Tang K (2013) Scaling up covariance matrix adaptation evolution strategy using cooperative coevolution. In: International conference on intelligent data engineering and automated learning. Springer, pp 350–357
[22]
Mahdavi S, Shiri ME, Rahnamayan S (2014) (2014) Cooperative co-evolution with a new decomposition method for large-scale optimization. IEEE Congress on evolutionary computation (CEC). IEEE, pp 1285–1292
[23]
Mei Y, Omidvar MN, Li X, and Yao X A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization ACM Trans Math Softw 2016 42 2 13
[24]
Montemurro M, Vincenti A, and Vannucci P A two-level procedure for the global optimum design of composite modular structures-application to the design of an aircraft wing J Optim Theory Appl 2012 155 1 24-53
[25]
Montemurro M, Vincenti A, and Vannucci P The automatic dynamic penalisation method (ADP) for handling constraints with genetic algorithms Comput Methods Appl Mech Eng 2013 256 70-87
[26]
Mosharmovahhed M and Moharrami H Design optimization of moment frame structures by the method of inscribed hyperspheres Struct Multidisc Optim 2021 64 1 335-348
[27]
Munetomo M and Goldberg DE Identifying linkage groups by nonlinearity/non-monotonicity detection Proc Genet Evolut Comput Conf 1999 1 433-440
[28]
Omidvar MN, Li X, Mei Y, and Yao X Cooperative co-evolution with differential grouping for large scale optimization IEEE Trans Evol Comput 2014 18 3 378-393
[29]
Omidvar MN, Yang M, Mei Y, Li X, and Yao X DG2: a faster and more accurate differential grouping for large-scale black-box optimization IEEE Trans Evol Comput 2017 21 6 929-942
[30]
Pavlovčič L, Krajnc A, and Beg D Cost function analysis in the structural optimization of steel frames Struct Multidisc Optim 2004 28 4 286-295
[31]
Ray T, Yao X (2009) A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning. In: IEEE Congress on evolutionary computation CEC’09. IEEE, pp 983–989
[32]
Saka M Optimum design of steel frames using stochastic search techniques based on natural phenomena: a review Civ Eng Comput 2007 6 105-147
[33]
Santana R (2017) Gray-box optimization and factorized distribution algorithms: where two worlds collide. http://arxiv.org/abs/1707.03093
[34]
Slowik A and Kwasnicka H Nature inspired methods and their industry applications-swarm intelligence algorithms IEEE Trans Ind Inf 2018 14 3 1004-1015
[35]
Storn R and Price K Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces J Glob Optim 1997 11 4 341-359
[36]
Sun L, Yoshida S, Cheng X, and Liang Y A cooperative particle swarm optimizer with statistical variable interdependence learning Inf Sci 2012 186 1 20-39
[37]
Talatahari S, Gandomi AH, Yang XS, and Deb S Optimum design of frame structures using the eagle strategy with differential evolution Eng Struct 2015 91 16-25
[38]
Tintos R, Whitley D, Chicano F (2015) Partition crossover for pseudo-boolean optimization. In: Proceedings of the 2015 ACM conference on foundations of genetic algorithms XIII, ACM, pp 137–149
[39]
Whitley D, Hains D, Howe A (2010) A hybrid genetic algorithm for the traveling salesman problem using generalized partition crossover. In: International conference on parallel problem solving from nature. Springer, pp 566–575

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          Published In

          cover image Structural and Multidisciplinary Optimization
          Structural and Multidisciplinary Optimization  Volume 66, Issue 1
          Jan 2023
          618 pages

          Publisher

          Springer-Verlag

          Berlin, Heidelberg

          Publication History

          Published: 27 December 2022
          Accepted: 07 October 2022
          Revision received: 17 September 2022
          Received: 26 July 2021

          Author Tags

          1. Engineering optimization
          2. Problem structure
          3. Gray-box optimization
          4. Variable interaction analysis
          5. Evolutionary computation

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