A Distance Convergence And History Density Based Multi-objective Evolutionary Algorithm

Recently,multiobjective optimization problems have been the most important problem in the field of artificial intelligence.Based on the effective solving power,multiobjective evolutionary algorithms have become the most popular approach for solving the multiobjective optimization problems.Multiobjective evolutionary algorithm is artificial intelligence algorithm based on population evolution.In the process of selecting individuals,most multi-objective evolutionary algorithms use nondominated information and density information to evaluate individuals.Aiming at the above problem,we propose a distance convergence and history density based multi-objective evolutionary algorithm,which is compared with 4 multi-objective evolutionary algorithms in 2 benchmarks.The main work of this paper can be summarized as follows:(1)In order to effectively distinguish the degree of convergence of individuals in the same level of dominance,the concept of distance convergence is proposed;(2)In order to calculate the density of individuals in the entire search process,a new mating selection approach is proposed;(3)The experimental results show that the proposed algorithm is better than the contrast algorithms.