Abstract: Optimization problem with large-scale decision variable is one of the hot and difficult points in the multi-objective evolutionary algorithm research field.When solving the problem of large-scale variable,the current evolutionary algorithm does not find the related information between decision variables and treats all the decision variables as a whole to optimize.But the variable dimensionality will become the bottleneck as the decision variables in the optimization problem increase,which will affect the performance of the algorithm.To settle these problems,this paper proposed a interacting variable grouping strategy to identify the internal relation among the decision variables and allocate the inte-racting variables to the same group.Thus,it can decompose a difficult high-dimensional problem into a set of simpler and low-dimensional subproblems that are easier to solve.In order to make the algorithm as far as possible to retain the relationship between variables and keep the interdependencies among different subproblems minimal,this strategy increases the probability of assigning the interacting variables to the same group so as to improve the quality of the optimal solution of the subproblems and ultimately gets the best Pareto optimal solution set.Comparative simulation experiment was conducted after the variable extension on standard test function.The convergence and diversity of the algorithm were compared and analyzed using a variety of performance indicators.Experiment results show that this algorithm can produce higher quality Pareto optimal solution set and is of better convergence and distribution than the classical multi-objective evolutionary algorithms like NSGA-II,MOEA/D and RVEA as the dimension of decision variables increases in multi-objective optimization problem with large-scale variable.

Key words: Large-scale optimization,Interacting variables,Variable identification,Grouping decomposition