A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

The current literature of evolutionary many-objective optimization is merely focused on the scalability to the number of objectives, while little work has considered the scalability to the number of decision variables. Nevertheless, many real-world problems can involve both many objectives and large-scale decision variables. To tackle such large-scale many-objective optimization problems (MaOPs), this paper proposes a specially tailored evolutionary algorithm based on a decision variable clustering method. To begin with, the decision variable clustering method divides the decision variables into two types: 1) convergence-related variables and 2) diversity-related variables. Afterward, to optimize the two types…

• EC
  European Commission
• NN
  National Natural Science Foundation of China

  Awards: 61428302, 61672033, 61502004, 61272152, 61502001
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/M017869/1, EP/M017869/1