A general purpose real-world structural design optimization computing platform

Structural optimization has matured from a narrow academic discipline, where researchers focused on optimum design of small idealized structural components and systems, to become the basis in modern design of complex structural systems. Some software applications in recent years have made these tools accessible to professional engineers, decision-makers and students outside the structural optimization research community. These software applications, mainly focused on aerospace, aeronautical, mechanical and naval structural systems, have incorporated the optimization component as an additional feature of the finite element software package. On the other hand though there is not a holistic optimization approach in terms of final design stage for real-world civil engineering structures such as buildings, bridges or more complex civil engineering structures. The optimization computing platform presented in this study is a generic real-world optimum design computing platform for civil structural systems and it is implemented within an innovative computing framework, founded on the current state of the art in topics like metaheuristic optimization, structural analysis and parallel computing. For demonstration purposes the application of the optimization computing platform in five real-world design projects is presented.

[1]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[2]  Dimitrios Vamvatsikos,et al.  Incremental dynamic analysis , 2002 .

[3]  Ahmed Ghobarah,et al.  Response‐based damage assessment of structures , 1999 .

[4]  Nils Aall Barricelli,et al.  Numerical testing of evolution theories , 1963 .

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Nikos D. Lagaros,et al.  Evolution Strategies-Based Metaheuristics in Structural Design Optimization , 2013 .

[7]  Manolis Papadrakakis,et al.  Structural design optimization considering uncertainties , 2008 .

[8]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[9]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[10]  Nikos D. Lagaros,et al.  Multicomponent incremental dynamic analysis considering variable incident angle , 2010 .

[11]  G. I. Schuëller,et al.  Developments in stochastic structural mechanics , 2006 .

[12]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[13]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[14]  Miltiadis Kotinis,et al.  Multi-objective shape optimization of transonic airfoil sections using swarm intelligence and surrogate models , 2012 .

[15]  Zong Woo Geem,et al.  Recent Advances In Harmony Search Algorithm , 2010, Recent Advances In Harmony Search Algorithm.

[16]  Manolis Papadrakakis,et al.  Multi-database exploration of large design spaces in the framework of cascade evolutionary structural sizing optimization , 2005 .

[17]  Klaus Schittkowski,et al.  Software systems for structural optimization , 1993 .

[18]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[19]  Mohammad Reza Farmani,et al.  Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization , 2011 .

[20]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[21]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[22]  Nikos D. Lagaros,et al.  A critical assessment of metaheuristics for scheduling emergency infrastructure inspections , 2011, Swarm Evol. Comput..

[23]  Dan Frangopol.  Structural Design Optimization Considering Uncertainties: Structures & Infrastructures Book , Vol. 1, Series, Series Editor: Dan M. Frangopol , 2008 .

[24]  Russell C. Eberhart,et al.  Evolutionary Computation Theory and Paradigms , 2001 .

[25]  Manolis Papadrakakis,et al.  Applied soft computing for optimum design of structures , 2012 .

[26]  Xin-She Yang,et al.  Metaheuristic Optimization: Algorithm Analysis and Open Problems , 2011, SEA.

[27]  D. Weaire,et al.  A counter-example to Kelvin's conjecture on minimal surfaces , 1994 .

[28]  Mervyn J. Kowalsky,et al.  THE LIMITATIONS AND PERFORMANCES OF DIFFERENT DISPLACEMENT BASED DESIGN METHODS , 2003 .

[29]  Rodolphe Le Riche,et al.  On global optimization articles in SMO , 2012 .

[30]  Luis Esteva,et al.  Comparing the adequacy of alternative ground motion intensity measures for the estimation of structural responses , 2004 .

[31]  Efstratios Nikolaidis,et al.  First- and Second-Order Reliability Methods , 2004 .

[32]  Antonin Ponsich,et al.  Differential Evolution performances for the solution of mixed-integer constrained process engineering problems , 2011, Appl. Soft Comput..

[33]  William Thomson.  On the division of space with minimum partitional area , 1887 .

[34]  A. Kiureghian,et al.  Multivariate distribution models with prescribed marginals and covariances , 1986 .

[35]  Stefan Reh,et al.  Probabilistic finite element analysis using ANSYS , 2006 .

[36]  Stefan Roth,et al.  Covariance Matrix Adaptation for Multi-objective Optimization , 2007, Evolutionary Computation.

[37]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.