Large-scale multi-objective evolutionary optimization based on direction vector sampling
To tackle the difficulty to search for promising offspring individuals in large-scale multi-objective optimization of high-dimensional decision space,which leads to the deterioration of the performance of conventional algorithms,a large-scale multi-objective evolutionary algorithm based on direction vector sampling is proposed.The algorithm first selects some good candidate individuals close to the ideal point,then creates direction vectors in stages,and executes directional sampling strategies,that is,convergence-related sampling strategy and diversity-related sampling strategy to generate offspring individuals.In this way,the offspring individuals with better performance are generated to accelerate the convergence of the algorithm.Experimental results show that compared with five representative large-scale multi-objective evolutionary algorithms,the proposed algorithm has retained strong optimizing ability on large-scale multi-objective optimization test problems with as many as 2 000 decision variables.
万方数据
维普
