基于广义改进分解策略的多目标代理优化方法

Multi-objective surrogate-based optimization method based on general improvement decomposition strategy

为解决多目标代理优化方法中代理模型选择单一问题,提出基于广义改进函数分解策略的多目标代理优化方法.该方法充分利用模型预测信息构建广义改进多目标分解准则和广义改进R2指标准则,有效拓展多目标代理优化中代理模型的选择空间.所提两种准则通过随机均匀权重实现全局探索和局部搜索能力的自适应平衡.研究结果表明,所提方法在有限仿真条件下拥有良好的寻优性能,获得Pareto前沿在收敛性、多样性及空间分布性方面均具有一定优势.相比同类方法,该方法具有优势:(1)不需要模型预测不确定性信息,适用于基于不同种类代理模型的代理优化方法;(2)实现简单且计算复杂度低,能够有效提升昂贵黑箱问题优化效率.

To solve the problem that the surrogate is usually limited to a single type,a multi-objective surrogate-based optimization method based on general improvement decomposition strategy is proposed.In this method,the model prediction value information is fully used to construct the general improvement multi-objective decomposition criterion and the general improvement R2 indicator criterion,thus expanding the selection scope of surrogate models in multi-objective surrogate-based optimization methods.The two proposed criteria can achieve an adaptive balance between global exploration and local exploitation with random uniform weights.Comparison results show that the proposed method has good optimization performance under limited simulation conditions,and the Pareto front has certain advantages in convergence,diversity,and spatial distribution.Compared with similar methods,the proposed method has the following advantages:(1)it is suitable for many different surrogate-based optimization methods because the uncertainty of the model prediction is unnecessary,(2)its implementation is simple and computational complexity is low,which can significantly improve the optimization efficiency of expensive black-box problems.