A Surrogate-assisted Hybrid Algorithm with Semi-supervised Learning for Expensive Optimization Problems
It is difficult to get sufficient data to train a global surrogate model in a limited computational budget when the objective of an optimization problem is expensive to be evaluated.However,a potential ad-vantage of the global surrogate model is that it can smooth out the local optima of the problem,which can speed up the method to find the optimal solution.On the other hand,a local surrogate model is not able to jump out from the local optimum,but it has a better performance to fit the original expensive problem in the local region.Thus,in this thesis,we propose a surrogate-assisted hybrid algorithm with semi-supervised learn-ing(SSL-SAHA)for sovling expensive optimization problems.Based on the existing algorithms,the local search is modified.The surrogate ensemble built in the global search is used for choosing some solutions,which have not been evaluated using the exact expensive objective function,to join the local surrogate model training,being expected to improve the accuracy of the local RBF surrogate model.The results show that the proposed method can effectively solve the computationally expensive problem.
万方数据
维普
