Large-scale structural optimization using metaheuristic algorithms with elitism and a filter strategy

Large-scale structural optimization often requires numerous finite element analyses to assess the feasibility of the derived solutions during the optimization process, which consume most of the computational cost. To enhance the computational efficiency, this study introduces a filter strategy aiming to eliminate the redundant constraint violation evaluations in large-scale structural optimization using metaheuristic algorithms. Based on the solution selection rule, this study separates the metaheuristic algorithms into two categories: replacement and elitism. The filter mechanism founds on elitism of the metaheuristic algorithms and reduces substantially the number of structural analyses without compromising the effectiveness of the optimization algorithms and the constraint handling techniques. This study also defines a parameter, R, to assess the enhancement performance of the computational efficiency improved by the proposed method. Results from both mathematical simulations and two large-scale structural optimization examples using various metaheuristic algorithms demonstrate that the harmony search (HS) leads always to the lowest R value. The R value is less than 0.4 and is even as small as 0.09 for the 942-bar example, which means over 90% of time savings compared with the penalty method and the Deb rule and the quality of the final optimum also does not depend on the value of R.

[1]  Akhil Upadhyay,et al.  Computationally efficient analysis of cable-stayed bridge for GA-based optimization , 2009, Eng. Appl. Artif. Intell..

[2]  Ali Kaveh,et al.  A new metaheuristic for continuous structural optimization: water evaporation optimization , 2016 .

[3]  Mohamed Abdel-Baset,et al.  A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems , 2014 .

[4]  Saeid Kazemzadeh Azad,et al.  Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses , 2018 .

[5]  Joong Hoon Kim,et al.  Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy , 2016 .

[6]  Efrn Mezura-Montes,et al.  Constraint-Handling in Evolutionary Optimization , 2009 .

[7]  R. Kowalczyk,et al.  Constraint consistent genetic algorithms , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[8]  Carlos A. Coello Coello,et al.  Constraint-handling in nature-inspired numerical optimization: Past, present and future , 2011, Swarm Evol. Comput..

[9]  M. Ilchi Ghazaan,et al.  A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures , 2018 .

[10]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[11]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[12]  Mohammad Mehdi Ebadzadeh,et al.  A novel particle swarm optimization algorithm with adaptive inertia weight , 2011, Appl. Soft Comput..

[13]  G. Gary Wang,et al.  Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions , 2010 .

[14]  Hongping Zhu,et al.  An Iterative Calculation Method for Suspension Bridge's Cable System Based on Exact Catenary Theory , 2013 .

[15]  Anne Raich,et al.  Design and optimization of steel trusses using genetic algorithms, parallel computing, and human-computer interaction , 2006 .

[16]  Jaroslaw Sobieszczanski-Sobieski,et al.  Particle swarm optimization , 2002 .

[17]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[18]  J R Saunders,et al.  A particle swarm optimizer with passive congregation. , 2004, Bio Systems.

[19]  Ruben E. Perez,et al.  Constrained structural design optimization via a parallel augmented Lagrangian particle swarm optimization approach , 2011 .

[20]  Hongping Zhu,et al.  Improved Particle Swarm Optimization-Based Form-Finding Method for Suspension Bridge Installation Analysis , 2015 .

[21]  Charles V. Camp,et al.  Multi-class teaching–learning-based optimization for truss design with frequency constraints , 2016 .

[22]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[23]  Tamara Nestorović,et al.  Finite element model updating using simulated annealing hybridized with unscented Kalman filter , 2016 .

[24]  Hongping Zhu,et al.  Enhanced particle swarm optimization for size and shape optimization of truss structures , 2017 .

[25]  A. Rezaee Jordehi,et al.  A review on constraint handling strategies in particle swarm optimisation , 2015, Neural Computing and Applications.

[26]  S. O. Degertekin.  Improved harmony search algorithms for sizing optimization of truss structures , 2012 .

[27]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

[28]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[29]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[30]  O. Hasançebi,et al.  Upper bound strategy for metaheuristic based design optimization of steel frames , 2013, Adv. Eng. Softw..

[31]  Fan Ye,et al.  Two-level Multi-surrogate Assisted Optimization method for high dimensional nonlinear problems , 2016, Appl. Soft Comput..

[32]  David A. Roke,et al.  Optimization of retaining wall design using recent swarm intelligence techniques , 2015 .

[33]  Wei Wang,et al.  A Filter-Genetic Algorithm for Constrained Optimization Problems , 2015 .

[34]  Qian Wang,et al.  Review of formulations for structural and mechanical system optimization , 2005 .

[35]  Liqun Gao,et al.  An improved particle swarm optimization algorithm for reliability problems. , 2011, ISA transactions.

[36]  Kalyanmoy Deb,et al.  An improved fully stressed design evolution strategy for layout optimization of truss structures , 2016 .

[37]  O. Hasançebi,et al.  Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures , 2008 .

[38]  Kaisa Miettinen,et al.  On initial populations of a genetic algorithm for continuous optimization problems , 2007, J. Glob. Optim..

[39]  Víctor Yepes,et al.  Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges , 2015 .

[40]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

[41]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[42]  Saeid Kazemzadeh Azad,et al.  Enhanced hybrid metaheuristic algorithms for optimal sizing of steel truss structures with numerous discrete variables , 2017 .

[43]  O. Hasançebi,et al.  Upper Bound Strategy in Optimum Design of Truss Structures: A Big Bang-Big Crunch Algorithm Based Application , 2013 .

[44]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[45]  Mahmoud R. Maheri,et al.  Sizing Optimization of Truss Structures by an Efficient Constraint-Handling Strategy in TLBO , 2017, J. Comput. Civ. Eng..

[46]  R. Haftka,et al.  Constrained particle swarm optimization using a bi-objective formulation , 2009 .

[47]  T. Bakhshpoori,et al.  An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm , 2014 .

[48]  Raphael T. Haftka,et al.  A Segregated Genetic Algorithm for Constrained Structural Optimization , 1995, ICGA.

[49]  Xin Wang,et al.  An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems , 2016, J. Intell. Manuf..

[50]  K. R. Leimbach,et al.  Optimal design of truss structures using parallel computing , 2005 .

[51]  A. Kaveh,et al.  Optimal design of large-scale space steel frames using cascade enhanced colliding body optimization , 2017 .

[52]  O. Hasançebi,et al.  On efficient use of simulated annealing in complex structural optimization problems , 2002 .

[53]  Hongping Zhu,et al.  Layout and size optimization of suspension bridges based on coupled modelling approach and enhanced particle swarm optimization , 2017 .