Paper Info
Title
Development and testing of a morphological geometric representation scheme for topology design optimization using a genetic algorithm
Abstract
This work describes a novel way of defining structural geometry for solving topology design optimization problems using a genetic algorithm (GA). It is a geometric representation scheme that works by specifying a skeleton which defines the underlying topology/connectivity of a structural continuum together with segments of material surrounding the skeleton. The required design variables are encoded in a chromosome which is in the form of a directed graph that embodies this underlying topology so that appropriate crossover and mutation operators can be devised to recombine and help preserve any desirable geometry characteristics of the design through succeeding generations in the evolutionary process. The overall methodology is first tested by solving a 'target geometry matching problem' - a simulated topology optimization problem in which a 'target' geometry is pre-defined as the optimum solution, and the objective of the optimization problem is to evolve design solutions to converge towards this 'target' shape. The second test problem is to design a complaint mechanism - a large-displacement flexural structure that undergoes a desired displacement path at some point when given a straight line input loading at some other point - by a process of topology/shape optimization.
Year
DOI
Venue
2003
10.1109/CEC.2003.1299638
IEEE Congress on Evolutionary Computation (1)
Keywords
Field
DocType
mutation operators,straight line input loading,morphological geometric representation,design solutions,crossover operators,displacement path,complaint mechanism,test problem,design variables,structural engineering,structural continuum,geometric representation scheme,mathematical morphology,geometry characteristics,chromosome,optimization problem,genetic algorithm,directed graph,evolutionary process,genetic algorithms,directed graphs,structural geometry,simulated topology optimization,dna,target geometry matching problem,geometry,topology design optimization,large-displacement flexural structure,shape optimization,optimum solution,topology optimization
Line (geometry),Mathematical optimization,Crossover,Computer science,Shape optimization,Topology optimization,Extension topology,Optimization problem,Genetic algorithm,Computational topology
Conference
Volume
ISBN
Citations 
1
0-7803-7804-0
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
K. Tai117722.25
Shamim Akhtar291.73