Publications · 2010 · Conference paper
Two Novel Approaches for Many-Objective Optimization
In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), 2010
Abstract
In this paper, two novel evolutionary approaches for many-objective optimization are proposed. These algorithms integrate a fine-grained ranking of solutions to favor convergence, with explicit methodologies for diversity promotion in order to guide the search towards a representative approximation of the Pareto-optimal surface. In order to validate the proposed algorithms, we performed a comparative study where four state-of-the-art representative approaches were considered. In such a study, four well-known scalable test problems were adopted as well as six different problem sizes, ranging from 5 to 50 objectives. Our results indicate that our two proposed algorithms consistently provide good convergence as the number of objectives increases, outperforming the other approaches with respect to which they were compared.
BibTeX
@inproceedings{Fabre10a,
author = {Mario Garza Fabre and Gregorio Toscano Pulido and Carlos A. {Coello Coello}},
title = {{Two Novel Approaches for Many-Objective Optimization}},
booktitle = {2010 IEEE Congress on Evolutionary Computation (CEC'2010)},
year = {2010},
pages = {4480--4487},
address = {Barcelona, Spain},
month = {July 18--23},
publisher = {IEEE Press},
doi = {10.1109/CEC.2010.5585930},
url = {http://doi.org/10.1109/CEC.2010.5585930},
issn = {1089-778X},
}