A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, wh...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 23; číslo 1; s. 74 - 88
Hlavní autoři: Pan, Linqiang, He, Cheng, Tian, Ye, Wang, Handing, Zhang, Xingyi, Jin, Yaochu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Classification
Computer simulation
Evolutionary algorithms
Evolutionary computation
expensive many-objective optimization
Fitness
Genetic algorithms
Linear programming
Multiple objective analysis
Neural networks
Neurons
Optimization
Pareto dominance
Prediction algorithms
Signal processing algorithms
State of the art
surrogate-assisted evolutionary optimization
Uncertainty
ISSN:1089-778X, 1941-0026
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Shrnutí:Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2802784