A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive referen...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 22; číslo 1; s. 129 - 142
Hlavní autoři: Chugh, Tinkle, Jin, Yaochu, Miettinen, Kaisa, Hakanen, Jussi, Sindhya, Karthik
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.02.2018
Témata:
Approximation algorithms
Computational cost
Computational modeling
Convergence
Evolutionary computation
Kriging
Linear programming
model management
multiobjective optimization
Optimization
reference vectors
surrogate-assisted evolutionary algorithms (SAEAs)
Training
ISSN:1089-778X, 1941-0026
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Shrnutí:We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art SAEAs on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2016.2622301