A Dynamic Penalty Function within MOEA/D for Constrained Multi-objective Optimization Problems

For more than a decade, the efficiency and effectiveness of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition) when solving complicated problems has been shown. Due to this, several researchers have focused their investigations on MOEA/D's extensions that can deal with CMOPs (Constrained Multi-objective Optimization Problems). In this paper, we adhere to the MOEA/D framework, a simple penalty function to deal with CMOPs. The penalty function is dynamically adapted during the search. In this way, the interaction between feasible and infeasible solutions is promoted. As a result, the proposed approach (namely MOEA/D-DPF) extends MOEA/D to handle constraints. The proposed approach performance is evaluated on the well-known CF test problems taken from the CEC'2009 suite. Using convergence and feasibility indicators, we compare the solutions produced by our algorithm against those produced by state-of-the-art MOEAs. Results show that MOEA/D-DPF is highly competitive and, in some cases, it performs better than the MOEAs adopted in our comparative study.