Constrained multi-objective differential evolution algorithm with ranking mutation operator

There are feasible and infeasible solutions in Constrained Multi-objective Optimization Problems (CMOPs). The feasible solutions with lower rank should be given more chances to generate offspring, while infeasible and worse solutions with higher rank should have fewer chances in these CMOPs. A constrained multi-objective Differential Evolution (DE) algorithm is developed by considering the selection pressure. The population is ranked based on the non-dominated crowd sort and constrained dominated principle. Then, a tournament operator is designed to extend the conventional mutation operator to boost the exploitation. The performances of the proposed algorithm are assessed on nineteen benchmark functions and industrial applications. Five representative algorithms are selected to make comparisons. The experiments have demonstrated that the algorithm can find well-distributed Pareto front, and the result of the performance indicator is superior.