求解高维混合指标优化问题的交互式进化计算

同时存在区间显式指标和模糊隐式指标的高维混合指标优化问题是一类难以求解的不确定多目标优化问题.针对该问题，首先，分别对高维显式指标和隐式指标的主要参数按确定性多目标优化，根据获得的相关权值，将高维显式指标和高维隐式指标分别降维成一维等效区间适应值和一维等效模糊适应值，二者合成个体等效指标体；然后，依据等效指标体的占优情况，通过确定自适应参考点和偏好区域面积选择个体；最后，在大规模种群NSGA-II范式下，采用隐式指标估计策略和种群聚类方法实现交互式进化优化算法.将本文算法应用于2种混合性能指标优化问题，验证所提算法的有效性和泛化性.

Sloving Multidimensional Optimization Problems with Hybird Indices by Interactive Evolutionary Computation

The multidimensional hybrid indices optimization problem is a kind of uncertainty multi-objective optimization problems that is difficult to solve.First,we can get relevant weights by optimizing the main parameters of explicit and implicit indices.According to these weights,multidimensional explicit indices can be reduced to an equivalent-interval fitness,and multidimensional implicit indices can be reduced to an equivalent-fuzzy fitness.Equivalent-interval fitness and equivalent-fuzzy fitness can be synthesized to an equivalent-index body.Then,we select advantage individual on the basis of equivalent-index bodies dominant situation according to adaptive reference point and preference area size.Finally,we adopt an implicit-indices estimation strategy with cluster method to realize interactive evolutionary algorithm within the framework of NSGA-II.The proposed algorithm is applied to two optimization problems with hybrid indices,and the results validate its efficiency and generalization.