Name
Title | ||
|---|---|---|
Target geometry matching problem with conflicting objectives for multiobjective topology design optimization using GA |
Abstract | ||
|---|---|---|
Genetic algorithms (GA) do have some advantages over gradient-based methods for solving topology design optimization problems. However, their success depends largely on the geometric representation used. In this work, an enhanced morphological representation of geometry is applied and evaluated to be efficient and effective in producing good results via a target matching problem: a simulated topology and shape design optimization problem where a dasiatargetpsila geometry set is first predefined as the Pareto optimal solutions and a multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set. As the objectives (and constraints) are conflicting, the problem is challenging and an adaptive constraint strategy is also incorporated in the GA to improve convergence towards the true Pareto front. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CEC.2008.4631044 | Evolutionary Computation, 2008. CEC 2008. |
Keywords | Field | DocType |
genetic algorithms,geometry,topology,Pareto front,genetic algorithms,geometric representation,geometry morphological representation,multiobjective optimization problem,multiobjective topology design optimization,shape design optimization problem,target geometry matching problem,topology design optimization problems | Convergence (routing),Mathematical optimization,Computer science,Delta modulation,Evolutionary computation,Multi-objective optimization,Solid modeling,Topology optimization,Geometry,Optimization problem,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
978-1-4244-1823-7 | 1 | 0.37 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Tai | 1 | 177 | 22.25 |
| Nianfeng Wang | 2 | 1 | 0.37 |
| Yaowen Yang | 3 | 1 | 1.38 |