An enhanced sparse multiobjective evolutionary algorithm in large-scale multiobjective optimization

In recent years, sparse large-scale multiobjective optimization problems (LSMOPs) have found widespread application in real-world scenarios and have become a focus of evolutionary computing research. Due to the high dimensionality of decision variables in LSMOPs, evolutionary algorithms (EAs) often struggle to efficiently find optimal solutions. In an effort to settle this difficulty, we raise an enhanced sparse multiobjective evolutionary algorithm (ESMOEA) that uses the strongly convex sparse (SCSparse) operator to optimize the decision variables, which can further enhance the sparsity of solutions. Additionally, to consider the sparsity property of solutions during variable grouping, the parameter in the sparse operator that represents whether the solution becomes sparse is ingeniously incorporated into the proposed sparse grouping technique. To evaluate the performance of the proposed ESMOEA, a set of experiments is carried out on both benchmark and real-world problems. The experimental results indicate that the proposed ESMOEA achieves superior performance compared to existing large-scale multiobjective evolutionary algorithms (MOEAs).