Journal
ENGINEERING OPTIMIZATION
Volume 43, Issue 5, Pages 541-557Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0305215X.2010.502935
Keywords
topology optimization; multi-objective evolutionary algorithm; ground element filtering; compliance minimization; population-based incremental learning
Funding
- Office of the Higher Education Commission, Thailand
- Industrial/University Cooperative Research Center in HDD Components, Khon Kaen University
Ask authors/readers for more resources
This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives like structural compliance, natural frequency and mass, and subjected to constraints on stress, etc., are posed for performance testing. The MOEAs include Pareto archive evolution strategy (PAES), population-based incremental learning (PBIL), non-dominated sorting genetic algorithm (NSGA), strength Pareto evolutionary algorithm (SPEA), and multi-objective particle swarm optimization (MPSO). The various MOEAs are implemented to solve the problems. The ground element filtering (GEF) technique is used to suppress checkerboard patterns on topologies. The results obtained from the various optimizers are illustrated and compared. It is shown that PBIL is far superior to the others. The optimal topologies from using PBIL can be compared with those obtained by employing the classical gradient-based approach. It can be considered as a powerful tool for structural topological design.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available