Name
Title | ||
|---|---|---|
An Enhanced Chromosome Encoding And Morphological Representation Of Geometry For Structural Topology Optimization Using Ga |
Abstract | ||
|---|---|---|
The structural topology optimization approach can be used to generate the structural design for some desired input-output (force-deflection) requirements. Optimization methods based on genetic algorithms (GA) have recently been demonstrated to have the potential for overcoming the problems associated with gradient-based methods. The success of the GA depends, to a large extent, on the structural geometry representation scheme used. In this work, some enhancements are incorporated into the recently developed morphological geometric representation scheme coupled with a GA. Based on the morphology of living creatures, a geometric representation scheme had earlier been developed that works by specifying a skeleton which defines the underlying topology/connectivity of a structural continuum together with segments of material surrounding the skeleton. In this work, the flexibility to turn on or off parts of the skeleton is integrated into the scheme. This improves the variability of topological and shape characteristics in the evolutionary process and enhances the representation's versatility. The methodology is tested by solving a multicriterion 'target matching' problem : a simulated topology optimization problem where a 'target' geometry is first created and predefined as the optimum solution, and design solutions are evolved to converge towards this target shape. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4425016 | 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS |
Keywords | Field | DocType |
evolutionary computation,structural engineering,topology optimization,genetic algorithm,input output,genetic algorithms | Chromosome encoding,Computer science,Artificial intelligence,Topology optimization,Geometric representation,Geometric topology,Geometry,Genetic algorithm,Creatures,Mathematical optimization,Evolutionary computation,Genetic representation,Machine learning | Conference |
Citations | PageRank | References |
6 | 0.60 | 1 |
Authors | ||
2 | ||