采用两步训练法的多目标分布估计算法

提出两步训练法,改进了基于规则模型的多目标分布估计算法.在算法的模型训练环节,首先采用均值分簇法进行初步聚类;然后采用基于流形分簇法进行细致聚类,代替原算法中采用局部主元分析算法需要循环迭代的聚类分簇方法.通过6个Benchmark测试函数验证,改进算法保持了原算法的收敛性和多样性,并缩短了寻优的时间.

Regularity model-based multiobjective estimation of distribution algorithm with two steps training method

The two steps training method is proposed to replace the iterative way of local principal component analysis
algorithm in the regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA). In the phase of
training model of improvement algorithm, the ??-means clustering method is used to partition the points in population into
primary ?? disjoint clusters at the first step, and then the clustering method based manifold is used to partition at the second
step. Simulation results of the six benchmark instances show that the improvement algorithm can maintain the convergence
and diversity performance, and decline the computation time greatly.