Randomized line search techniques in combined GA for discrete sizing optimization of truss structures

This paper presents two randomized line search techniques, each combined with a genetic algorithm (GA), to improve the convergence and the accuracy ratio for discrete sizing optimization of truss structures. The first technique is a simple one-dimensional line search in which design variable axes are selected randomly as search directions. The second is a line search technique whose search direction is determined randomly by fitness function values and differences in the genotypes of individuals. To apply the above-mentioned line search techniques without difficulty, real coding is adopted for discrete problems. The line search techniques are applied to discrete optimization problems of minimum-weight truss structures subjected to stress and displacement constraints. The proposed techniques provide convergence to better solutions than a conventional GA.

[1]  Shyue-Jian Wu,et al.  Integrated discrete and configuration optimization of trusses using genetic algorithms , 1995 .

[2]  I-Cheng Yeh.  Hybrid Genetic Algorithms for Optimization of Truss Structures , 1999 .

[3]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[4]  Kalyanmoy Deb,et al.  Optimal truss-structure design using real-coded genetic algorithms , 1998 .

[5]  Ting-Yu Chen,et al.  IMPROVEMENTS OF SIMPLE GENETIC ALGORITHM IN STRUCTURAL DESIGN , 1997 .

[6]  Zbigniew Michalewicz,et al.  Handling Constraints in Genetic Algorithms , 1991, ICGA.

[7]  Mariusz Pyrz.  Evolutionary Algorithm Integrating Stress Heuristics for Truss Optimization , 2004 .

[8]  Abhinav Gupta,et al.  A novel optimization approach for minimum cost design of trusses , 2007 .

[9]  W. Gutkowski,et al.  Controlled mutation in evolutionary structural optimization , 2001 .

[10]  G. Thierauf,et al.  Parallelization of the Evolution Strategy for Discrete Structural Optimization Problems , 1998 .

[11]  S Rajeev,et al.  GENETIC ALGORITHMS - BASED METHODOLOGY FOR DESIGN OPTIMIZATION OF TRUSSES , 1997 .

[12]  Charles V. Camp,et al.  Design of Space Trusses Using Ant Colony Optimization , 2004 .

[13]  K. Deb,et al.  Design of truss-structures for minimum weight using genetic algorithms , 2001 .

[14]  Pasquale Ponterosso,et al.  Heuristically Seeded Genetic Algorithms Applied to Truss Optimisation , 1999, Engineering with Computers.

[15]  Subramaniam Rajan,et al.  Sizing, Shape, and Topology Design Optimization of Trusses Using Genetic Algorithm , 1995 .

[16]  F. Herrera,et al.  Dynamic and heuristic fuzzy connectives‐based crossover operators for controlling the diversity and convergence of real‐coded genetic algorithms , 1996 .

[17]  M. Ohsaki.  Genetic algorithm for topology optimization of trusses , 1995 .

[18]  Izuru Takewaki,et al.  Application of an optimum design method to practical building frames with viscous dampers and hysteretic dampers , 2003 .

[19]  G. Vanderplaats,et al.  Survey of Discrete Variable Optimization for Structural Design , 1995 .

[20]  Mathias Stolpe,et al.  Global optimization of minimum weight truss topology problems with stress, displacement, and local buckling constraints using branch‐and‐bound , 2004 .

[21]  Shahram Pezeshk,et al.  Optimized Design of Two-Dimensional Structures Using a Genetic Algorithm , 1998 .

[22]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[23]  J. Abel,et al.  A two-step procedure for discrete minimization of truss weight , 1995 .

[24]  Mathias Stolpe,et al.  Global optimization of discrete truss topology design problems using a parallel cut-and-branch method , 2008 .

[25]  Li Chen,et al.  Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management , 1998 .

[26]  W. M. Jenkins.  A decimal-coded evolutionary algorithm for constrained optimization , 2002 .

[27]  Jan A. Snyman,et al.  A pseudo-discrete rounding method for structural optimization , 1996 .

[28]  Ayse T. Daloglu,et al.  An improved genetic algorithm with initial population strategy and self-adaptive member grouping , 2008 .

[29]  Hiroshi Ohmori,et al.  Truss topology optimization by a modified genetic algorithm , 2002 .

[30]  J. Bonet.  Recent developments in the incremental flow formulation for the numerical simulation of metal forming processes , 1998 .

[31]  Ali Kaveh,et al.  A HYBRID PARTICLE SWARM AND ANT COLONY OPTIMIZATION FOR DESIGN OF TRUSS STRUCTURES , 2008 .

[32]  Jasbir S. Arora,et al.  OPTIMAL DESIGN WITH DISCRETE VARIABLES: SOME NUMERICAL EXPERIMENTS , 1997 .

[33]  W. M. Jenkins,et al.  Towards structural optimization via the genetic algorithm , 1991 .

[34]  Feng Liu,et al.  A heuristic particle swarm optimization method for truss structures with discrete variables , 2009 .

[35]  Zbigniew Michalewicz,et al.  A Note on Usefulness of Geometrical Crossover for Numerical Optimization Problems , 1996, Evolutionary Programming.

[36]  S. Wu,et al.  Steady-state genetic algorithms for discrete optimization of trusses , 1995 .

[37]  Hyo Seon Park,et al.  Optimization of steel structures using distributed simulated annealing algorithm on a cluster of personal computers , 2002 .