Transactions of Nanjing University of Aeronautics & Astronautics
Evolutionary Algorithm with Ensemble Classifier Surrogate Model for Expensive Multiobjective Optimization
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Abstract:
For many real-world multiobjective optimization problems， the evaluations of the objective functions are computationally expensive. Such problems are usually called expensive multiobjective optimization problems （EMOPs）. One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations. Inspired from ensemble learning， this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier （MOEA-EC） for EMOPs. More specifically， multiple decision tree models are used as an ensemble classifier for the pre-selection， which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model. The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms. The experimental results show that MOEA-EC outperforms the compared algorithms.
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TP18
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