Sparse large-scale multi-objective optimization algorithm based on impact factor assistance

In the real world, there exists a special category of multi-objective optimization problems with more than 1000 decision variables. However, only a few decision variables play a crucial role in optimizing the objective functions. Such problems are defined as sparse large-scale multi-objective optimization problems (SLSMOPs). Due to the difficulty in effectively identifying the non-zero positions of decision variables, traditional evolutionary optimization algorithms suffer from slow convergence speed and poor convergence effect, which means it is unable to efficiently obtain the Pareto optimal solution set. To address this challenge, the Impact Factor Assisted Algorithm (IFA) is proposed, which adopts a novel initial population strategy to generate sparse populations. Meanwhile, the impact factor of each decision variable is calculated, serving as a key basis for measuring the importance of each decision variable. During the algorithm's operation, the impact factors are iteratively updated to rationally group decision variables and guide population evolution. This approach can accurately identify the positions of non-zero decision variables. The experimental results on eight benchmark and real-world problems indicate that the algorithm outperforms several existing sparse large-scale multi-objective optimization algorithms (SLSMOEAs).