RT Journal Article
T1 Adaptive Global WASF-GA to Handle Many-objective Optimization Problems
A1 González-Gallardo, Sandra
A1 Luque-Gallego, Mariano
A1 Saborido Infantes, Rubén
A1 Ruiz-Mora, Ana Belén
K1 Optimización
AB In this paper, a new version of the aggregation-based evolutionary algorithm Global WASF-GA (GWASF-GA) for many-objective optimization is proposed, called Adaptive Global WASF-GA (A-GWASF-GA). The fitness function of GWASF-GA is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, which considers two reference points (the nadir and utopian points) and a set of weight vectors. Despite of the benefits of using these two reference points simultaneously and a well- distributed set of weight vectors, it is necessary to go a step further to get better approximations in problems with complicated Pareto optimal fronts.For this, in A-GWASF-GA, some of the weight vectors are re-calculated during the optimization process based on the sparsity of the solutions found so far, and taking into account some theoretical results demonstrated in this paper regarding the ASF considered. Different strategies are carried out to accelerate the convergence and to maintain the diversity. The computational results, carried out in comparison with NSGA-III and different versions of MOEA/D, show the good performance of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems.
PB Elsevier
YR 2020
FD 2020
LK https://hdl.handle.net/10630/29799
UL https://hdl.handle.net/10630/29799
LA eng
NO Mariano Luque, Sandra Gonzalez-Gallardo, Rubén Saborido, Ana B. Ruiz, Adaptive Global WASF-GA to handle many-objective optimization problems, Swarm and Evolutionary Computation, Volume 54, 2020, 100644, ISSN 2210-6502, https://doi.org/10.1016/j.swevo.2020.100644. (https://www.sciencedirect.com/science/article/pii/S2210650218306187)
NO This work has been supported by the Spanish Ministry of Econ- omy and Competitiveness (project ECO2017-88883-R) co-financed by FEDER funds, and by the Regional Government of Andalucía (PAI group SEJ-532), This research has also been partially supported by grant num- bers TIN2017-88213-R (http://6city.lcc.uma.es) and RTC-2017-6714- 5 (http://ecoiot.lcc.uma.es) and by Universidad de Málaga, Campus Internacional de Excelencia Andalucía TECH. Sandra Gonzalez-Gallardo is recipient of a technical research contract within “Sistema Nacional de Garantía Juvenil y del Programa Operativo de Empleo Juvenil 2014– 2020 - Fondos FEDER” also acknowledges the training received from the University of Malaga PhD Programme in Economy and Business (Programa de Doctorado en Economía y Empresa de la Universidad de Málaga). Rubén Saborido is recipient of a Juan de la Cierva grant (refer- ence FJC2018-038537-I), funded by the Spanish State Research Agency.Ana B. Ruiz is recipient of the postdoctoral fellowship “Captación de Talento para la Investigación” at the Universidad de Málaga (Spain).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026