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
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南京理工大学经济管理学院

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中图分类号:

O 212.2;N 945.15

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Parallel surrogate-based optimization method for multi-objective problem based dual weighted constraint expectation improvement strategy
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School of economics and management, Nanjing University of Science and Technology

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Project Supported by the National Nature Science Foundation of China

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    摘要:

    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.

    Abstract:

    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.

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历史
  • 收稿日期:2021-05-14
  • 最后修改日期:2021-07-29
  • 录用日期:2021-07-30
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