The Effect of Feasible Region on Imbalanced Problem in Constrained Multi-objective Optimization

The decomposition-based multi-objective evolutionary algorithm, i.e. MOEA/D-M2M, has shown to be an efficient algorithm to solve unconstrained imbalanced multiobjective optimization problems. However, the use in constrained imbalanced multi-objective optimization problems has not been fully explored. In this paper, we study the factors that impact the constrained imbalanced multi-objective optimization problems. To begin with, a series of constrained imbalanced multi-objective optimization problems are constructed. Then three kinds of representative algorithms, i.e. NSGA-II, MOEA/D and MOEA/D-M2M, combined with the constraint domination principle respectively, are utilized to solve them. The experimental results demonstrate that MOEA/D-M2M works better than the other two compared algorithms on constrained imbalanced multi-objective optimization problems in terms of the reliability and stability of finding a set of well distributed non-domination solutions.