TY - GEN
T1 - A new hybrid metaheuristic for equality constrained bi-objective optimization problems
AU - Cuate, Oliver
AU - Uribe, Lourdes
AU - Ponsich, Antonin
AU - Lara, Adriana
AU - Beltran, Fernanda
AU - Sánchez, Alberto Rodríguez
AU - Schütze, Oliver
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The recently proposed Pareto Tracer method is an effective numerical continuation technique which allows performing movements along the set of KKT points of a given multi-objective optimization problem. The nature of this predictor-corrector method leads to constructing solutions along the Pareto set/front numerically; it applies to higher dimensions and can handle box and equality constraints. We argue that the right hybridization of multi-objective evolutionary algorithms together with specific continuation methods leads to fast and reliable algorithms. Moreover, due to the continuation technique, the resulting hybrid algorithm could have a certain advantage when handling, in particular, equality constraints. In this paper, we make the first effort to hybridize NSGA-II with the Pareto Tracer. To support our claims, we present some numerical results on continuously differentiable equality constrained bi-objective optimization test problems, to show that the resulting hybrid NSGAII/PT is highly competitive against some state-of-the-art algorithms for constrained optimization. Finally, we stress that the chosen approach could be applied to a more significant number of objectives with some adaptations of the algorithm, leading to a very promising research topic.
AB - The recently proposed Pareto Tracer method is an effective numerical continuation technique which allows performing movements along the set of KKT points of a given multi-objective optimization problem. The nature of this predictor-corrector method leads to constructing solutions along the Pareto set/front numerically; it applies to higher dimensions and can handle box and equality constraints. We argue that the right hybridization of multi-objective evolutionary algorithms together with specific continuation methods leads to fast and reliable algorithms. Moreover, due to the continuation technique, the resulting hybrid algorithm could have a certain advantage when handling, in particular, equality constraints. In this paper, we make the first effort to hybridize NSGA-II with the Pareto Tracer. To support our claims, we present some numerical results on continuously differentiable equality constrained bi-objective optimization test problems, to show that the resulting hybrid NSGAII/PT is highly competitive against some state-of-the-art algorithms for constrained optimization. Finally, we stress that the chosen approach could be applied to a more significant number of objectives with some adaptations of the algorithm, leading to a very promising research topic.
KW - Continuation methods
KW - Evolutionary algorithms
KW - Hybrid algorithms
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85063065527&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-12598-1_5
DO - 10.1007/978-3-030-12598-1_5
M3 - Contribución a la conferencia
SN - 9783030125974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 65
BT - Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
A2 - Coello Coello, Carlos A.
A2 - Deb, Kalyanmoy
A2 - Goodman, Erik
A2 - Klamroth, Kathrin
A2 - Reed, Patrick
A2 - Miettinen, Kaisa
A2 - Mostaghim, Sanaz
PB - Springer Verlag
T2 - 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019
Y2 - 10 March 2019 through 13 March 2019
ER -