O 212.2；N 945.15
School of economics and management, Nanjing University of Science and Technology
Project Supported by the National Nature Science Foundation of China
针对多目标仿真优化的高昂成本及黑箱函数难以获取问题, 提出了基于双重权约束期望改进策略的多目标并行代理优化方法. 首先, 该方法建立Kriging模型获取未试验点的预测不确定性; 其次, 构建双重权约束期望改进策略, 并利用填充策略矩阵及距离聚合方法实现新改进策略的聚合; 然后, 最大化聚合双重权约束期望改进策略实现多目标并行优化; 最后, 达到终止条件, 获得Pareto 最优解集. 选取测试函数及铰接夹芯梁设计案例进行优化验证. 对比结果表明: 所提方法可有效提升多目标问题优化效率, 减少昂贵仿真成本; 与同类方法相比, 低维问题中获取Pareto最优解集的收敛性、多样性及分布性更优.
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 dual weighted constraint expectation improvement strategy is proposed. Firstly, 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 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.