Multi-objective parallel surrogate-based optimization based on dual weighted constraint expectation improvement strategy

Considering the high computational cost in multi-objective simulation optimization and the difficulty of obtaining black box function, a multi-objective parallel surrogate-based optimization method based on the dual weighted constraint expectation improvement strategy is proposed. Firstly, the Kriging model is established to estimate the prediction uncertainty of untested points. Secondly, the dual weighted constraint expectation improvement strategy is constructed, and the new strategy is integrated by the infill strategy matrix and distance aggregation method. Then, the integration strategy is maximized to realize multi-objective parallel optimization; Finally, the Pareto optimal solution set is obtained when the termination condition reached. Test functions and pinned-pinned sandwich beam design cases are employed for optimization verification. Comparison and optimization results show that the proposed method can effectively improve the efficiency of multi-objective optimization. Compared with similar methods, the optimization results in low dimensional problems have better convergence, diversity and distribution.