Publications · 2003 · Conference paper
Multi-Objective Evolutionary Algorithms for Structural Optimization
In Computational Fluid and Solid Mechanics 2003. Proceedings of the Second MIT Conference on Computational Fluid and Solid Mechanics, 2003
Abstract
In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a microgenetic algorithm) for solving multiobjective optimization problems. Our approach uses three forms of elitism, including an external memory (or secondary population) to keep the nondominated solutions found along the evolutionary process. We validate our proposal using several engineering optimization problems taken from the specialized literature and compare our results with respect to two other algorithms (NSGA-II and PAES) using three different metrics. Our results indicate that our approach is very efficient (computationally speaking) and performs very well in problems with different degrees of complexity.
BibTeX
@inproceedings{Coello03a,
author = {Carlos A. {Coello Coello} and Gregorio {Toscano Pulido} and Arturo {Hernandez Aguirre}},
title = {Multi-{O}bjective {E}volutionary {A}lgorithms for {S}tructural {O}ptimization},
booktitle = {Computational Fluid and Solid Mechanics 2003. Proceedings of the Second MIT Conference on Computational Fluid and Solid Mechanics},
editor = {K.J. Bathe},
pages = {2244--2248},
publisher = {Elsevier},
volume = {2},
address = {The Netherlands},
month = {June},
year = {2003},
doi = {10.1007/s00158-005-0527-z},
url = {https://doi.org/10.1007/s00158-005-0527-z},
}