Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 23, Issue 1, Pages 74-88Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2802784
Keywords
Classification; expensive many-objective optimization; Pareto dominance; surrogate-assisted evolutionary optimization
Funding
- National Natural Science Foundation of China [61320106005, 61502004, 61502001, 61772214, 61672033]
- Innovation Scientists and Technicians Troop Construction Projects of Henan Province [154200510012]
- EPSRC [EP/M017869/1]
- EPSRC [EP/M017869/1] Funding Source: UKRI
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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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