TY - JOUR
T1 - A new hybrid evolutionary algorithm for the treatment of equality constrained MOPs
AU - Cuate, Oliver
AU - Ponsich, Antonin
AU - Uribe, Lourdes
AU - Zapotecas-Martínez, Saúl
AU - Lara, Adriana
AU - Schütze, Oliver
N1 - Publisher Copyright:
© 2019 by the authors.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Multi-objective evolutionary algorithms are widely used by researchers and practitioners to solve multi-objective optimization problems (MOPs), since they require minimal assumptions and are capable of computing a finite size approximation of the entire solution set in one run of the algorithm. So far, however, the adequate treatment of equality constraints has played a minor role. Equality constraints are particular since they typically reduce the dimension of the search space, which causes problems for stochastic search algorithms such as evolutionary strategies. In this paper, we show that multi-objective evolutionary algorithms hybridized with continuation-like techniques lead to fast and reliable numerical solvers. For this, we first propose three new problems with different characteristics that are indeed hard to solve by evolutionary algorithms. Next, we develop a variant of NSGA-II with a continuation method. We present numerical results on several equality-constrained MOPs to show that the resulting method is highly competitive to state-of-the-art evolutionary algorithms.
AB - Multi-objective evolutionary algorithms are widely used by researchers and practitioners to solve multi-objective optimization problems (MOPs), since they require minimal assumptions and are capable of computing a finite size approximation of the entire solution set in one run of the algorithm. So far, however, the adequate treatment of equality constraints has played a minor role. Equality constraints are particular since they typically reduce the dimension of the search space, which causes problems for stochastic search algorithms such as evolutionary strategies. In this paper, we show that multi-objective evolutionary algorithms hybridized with continuation-like techniques lead to fast and reliable numerical solvers. For this, we first propose three new problems with different characteristics that are indeed hard to solve by evolutionary algorithms. Next, we develop a variant of NSGA-II with a continuation method. We present numerical results on several equality-constrained MOPs to show that the resulting method is highly competitive to state-of-the-art evolutionary algorithms.
KW - Continuation method
KW - Equality constraints
KW - Evolutionary algorithm
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85079589101&partnerID=8YFLogxK
U2 - 10.3390/MATH8010007
DO - 10.3390/MATH8010007
M3 - Artículo
SN - 2227-7390
VL - 8
JO - Mathematics
JF - Mathematics
IS - 1
M1 - 7
ER -