In expensive optimization, function evaluations are based on expensive physical experiments or time consuming simulations. Moreover, the gradient for the objective is not readily available. Therefore, it is a challeng...
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In expensive optimization, function evaluations are based on expensive physical experiments or time consuming simulations. Moreover, the gradient for the objective is not readily available. Therefore, it is a challenge task to deal with expensive optimization. In this work, a decision space partition based surrogate-assisted evolutionary algorithm (DSP-SAEA) is proposed for expensive optimization. In DSP-SAEA, a two-stage search strategy is introduced, where the global search and the local search are seamlessly integrated. In the global search stage, a decision space partition based global search strategy is proposed. In this strategy, all the exactly evaluated points are clustered into a set of clusters. Thus, the decision space can be partitioned into several regions based on the formed clusters. Furthermore, in each region, the surrogate model is constructed. The algorithm will search for these regions simultaneously with the help of the built surrogate models. As a result, several promising points distributed in different regions are able to be obtained. In the local search stage, a model adaptive selection strategy and the trust region local search are integrated. The model adaptive selection strategy is introduced to accurately assist the trust region local search, where the local elite surrogate model is adaptively chosen from the local surrogate model pool. Experimental results on benchmark problems and the parameter estimation for frequency-modulated sound waves problem demonstrate that DSP-SAEA performs competitively compared with some state-of-the-art algorithms.
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