Ensemble surrogate assisted evolutionary algorithm for complex system many-objective optimization
Surrogate-Assisted Evolutionary Algorithms(SAEAs)are the most popular methods to solve the design optimization problems of expensive and complex engineering systems,which can accelerate the search for a set of Pareto solutions.However,the performance of the existing individual surrogate model is problem-dependent,and the predictive uncertainty will be increased with the increasing number of objectives.Therefore,an ensemble surro-gate assisted evolutionary algorithm for complex system many-objective optimization was proposed.The ensemble surrogate model combined with the reference vector replace mechanism was adopted to select the Pareto solutions further.The improving lower confidence bounder utility criterion and the adaptation of sampling selection strategy were used to choose new samples for the actual function evaluation.The newly added samples were used to update the ensemble surrogate model to find the best Pareto solutions.Compared with the existing algorithms,the algo-rithm's performance was verified by several used benchmark problems and practical engineering optimization prob-lems.The results showed that the proposed algorithm had well performance and potential.
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