2020
Luque, Mariano; Gonzalez-Gallardo, Sandra; Saborido, Rubén; Ruiz, Ana B
Adaptive Global WASF-GA to handle many-objective optimization problems Journal Article
In: Swarm and Evolutionary Computation, vol. 54, no. 100644, 2020, ISSN: 2210-6502.
Abstract | Links | BibTeX | Tags: Achievement scalarizing function, evolutionary algorithm, Many-objective optimization, Pareto optimal solutions, Weight vectors
@article{luque_adaptive_2020,
title = {Adaptive Global WASF-GA to handle many-objective optimization problems},
author = {Mariano Luque and Sandra Gonzalez-Gallardo and Rubén Saborido and Ana B Ruiz},
url = {http://www.sciencedirect.com/science/article/pii/S2210650218306187},
doi = {https://doi.org/10.1016/j.swevo.2020.100644},
issn = {2210-6502},
year = {2020},
date = {2020-05-01},
journal = {Swarm and Evolutionary Computation},
volume = {54},
number = {100644},
abstract = {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 RVEA, NSGA-III, and different versions of MOEA/D, show the potential of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems.},
keywords = {Achievement scalarizing function, evolutionary algorithm, Many-objective optimization, Pareto optimal solutions, Weight vectors},
pubstate = {published},
tppubtype = {article}
}
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 RVEA, NSGA-III, and different versions of MOEA/D, show the potential of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems.
2016
Saborido, Rubén; Ruiz, Ana B; Bermúdez, José D; Vercher, Enriqueta; Luque, Mariano
Evolutionary Multi-objective Optimization Algorithms for Fuzzy Portfolio Selection Journal Article
In: Appl. Soft Comput., vol. 39, no. C, pp. 48–63, 2016, ISSN: 1568-4946.
Links | BibTeX | Tags: Evolutionary multi-objective optimization, LR-fuzzy numbers, Pareto optimal solutions, Portfolio selection, Possibility distributions
@article{saborido_evolutionary_2016,
title = {Evolutionary Multi-objective Optimization Algorithms for Fuzzy Portfolio Selection},
author = {Rubén Saborido and Ana B Ruiz and José D Bermúdez and Enriqueta Vercher and Mariano Luque},
url = {http://dx.doi.org/10.1016/j.asoc.2015.11.005},
doi = {10.1016/j.asoc.2015.11.005},
issn = {1568-4946},
year = {2016},
date = {2016-01-01},
journal = {Appl. Soft Comput.},
volume = {39},
number = {C},
pages = {48--63},
keywords = {Evolutionary multi-objective optimization, LR-fuzzy numbers, Pareto optimal solutions, Portfolio selection, Possibility distributions},
pubstate = {published},
tppubtype = {article}
}