Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces

Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision…

• NN
  National Natural Science Foundation of China

  Award: 61673397
• CS
  Central South University

  Award: 2018CX010
• BA
  Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology

  Award: 2018IRS06