1（Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinghuangdao 066000,China）2（National Engineering Research Center for Equipment and Technology of Clod Strip Rolling,Yanshan University,Qinghuangdao 066000,China） 3（Jiamusi Electric Power Company,State Grid Heilongjiang Electric Power Company Limited,Jiamusi 154000,China）
Abstract：The Gaussian surrogate model accuracy is easily limited by the quality and quantity of data,and once the model is fixed,it can not adjust itself along with the progress of the algorithm.On the other hand,nearest neighbor can not meet the required precision because of the affection of the sample size in the beginning of the algorithm.So a NSGA-II algorithm is proposed to solve the problem of the right dominate relations of population individual for the interval multi-objective optimization with unknown optimization functions,which integrated Gaussian surrogate model and nearest neighbor.Firstly,Gaussian surrogate model is built up by training sample sets and the super-parameters are calculated by genetic algorithm,therefore,the possibility degree probability between candidate solutions are obtained according to Gaussian surrogate model;secondly,the similarity between candidate solutions and sample solutions is calculated by nearest neighbor,thus the possibility degree probability is obtained;finally,the regression memory method is dynamically used to adjust the weight of dominant results between Gaussian surrogate model and nearest neighbor to work the dominance relations of population individual out.The simulations results have verified the effectiveness of the designed algorithm.
2017, Vol. 38 
