Optimization of truss bridges within a specified design domain using evolution strategies

This article reports and investigates the application of evolution strategies (ESs) to optimize the design of truss bridges. This is a challenging optimization problem associated with mixed design variables, since it involves identification of the bridge’s shape and topology configurations in addition to the sizing of the structural members for minimum weight. A solution algorithm to this problem is developed by combining different variable-wise versions of adaptive ESs under a common optimization routine. In this regard, size and shape optimizations are implemented using discrete and continuous ESs, respectively, while topology optimization is achieved through a discrete version coupled with a particular methodology for generating topological variations. In the study, a design domain approach is employed in conjunction with ESs to seek the optimal shape and topology configuration of a bridge in a large and flexible design space. It is shown that the resulting algorithm performs very well and produces improved results for the problems of interest.

[1]  Manolis Papadrakakis,et al.  Parallel computational strategies for structural optimization , 2003 .

[2]  Robert G. Reynolds,et al.  A Survey of Constraint Handling Techniques in Evolutionary Computation Methods , 1995 .

[3]  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 .

[4]  Thomas Bäck,et al.  Evolution Strategies for Mixed-Integer Optimization of Optical Multilayer Systems , 1995, Evolutionary Programming.

[5]  Fuat Erbatur,et al.  Layout optimisation of trusses using simulated annealing , 2002 .

[6]  M. J. Garcia,et al.  Shape optimisation of continuum structures via evolution strategies and fixed grid finite element analysis , 2004 .

[7]  Ingo Rechenberg,et al.  Case studies in evolutionary experimentation and computation , 2000 .

[8]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[9]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[10]  A. F. Ulusoy,et al.  Discrete and continuous structural optimisation using evolution strategies , 2002 .

[11]  Carsten Ebenau,et al.  An advanced evolutionary strategy with an adaptive penalty function for mixed-discrete structural optimisation , 2005, Adv. Eng. Softw..

[12]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[13]  Jamshid Ghaboussi,et al.  Evolution of Optimum Structural Shapes Using Genetic Algorithm , 1998 .

[14]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

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

[16]  Yaowen Yang,et al.  Automated optimum design of structures using genetic programming , 2002 .

[17]  Raimondo Betti,et al.  Identification of Structural Systems using an Evolutionary Strategy , 2004 .

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