Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm

Table 1

Main steps of integrated surrogate-assisted model.

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Input: sample-set D, the predicted sample

Output: the model output value of the sample to be predicted;

Step 1: calculate the number of samples in , which is recorded as ;

Step 2: for to do

  Calculate the Euclidean distance between the sample and the ;

 End for

 Select the nearest 2/3 samples closest to the Euclidean distance of as the training set and all the remaining samples as the test set ;

Step 3: Gaussian process model and RBF neural network model are trained on the training set , respectively

Step 4: for to 2 do

  Calculate the test error of the model on the test set ;

 End for

Step 5: for to 2 do

  Calculate the weight of surrogate-assisted model , and ;

 End for

Step 6: outputs the final predicted value , where is the predicted value of the model at ;

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