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带拥挤距离排挤机制的非支配排序遗传算法(NSGA-II)在多目标优化领域具有广泛的应用,NSGA-II算法具有个体分布不均匀以及重复个体较多等缺陷.针对这些缺陷提出一种基于向量空间模型的NSGA-II改进算法VSMGA(Vector Space M odel Genetic Algorithm),VSM GA算法在NSGA-II算法的基础上引入了向量空间模型,利用目标权重向量之间的余弦距离代替原来的拥挤距离,提出一种距离排挤机制和重复个体排除规则.实验结果表明与NSGA-II算法比较,VSMGA算法具有更好的分布性和稳定性.
The non-dominated ranking genetic algorithm (GAGA-II) with crowding distance crowding mechanism is widely used in the field of multi-objective optimization (NSGA-II), and the NSGA-II algorithm has some disadvantages such as uneven distribution of individual and repeated individuals. NSGA-II algorithm of vector space model (VSMGA) and VSM GA algorithm introduce vector space model based on NSGA-II algorithm, and use the cosine distance of target weight vector instead of the original congestion distance , Proposed a distance crowding mechanism and repeated individual exclusion rules.The experimental results show that compared with NSGA-II algorithm, VSMGA algorithm has better distribution and stability.