A Comprehensive Comparison of Constrained Multi-Objective Evolutionary Algorithms

In order to solve constrained multi-objective optimization problems (CMOPs), many algorithms have been proposed in the evolutionary computation research community in the past two decades. In general, the effectiveness of an algorithm on CMOPs is evaluated by artificial test problems. At present, comprehensive performance comparisons of constraint-handling techniques on various test problems remain scarce. In this study, we selected 12 representative algorithms that integrate with different constraint-handling techniques and compared their performance on existing test suites. Performance comparisons identify the strengths and weaknesses of different constraint-handling techniques on different types of CMOPs, and provide guidance on how to select/design constraint-handling techniques in specific scenarios. In addition, after a careful review of current artificial test problems, we found out that their design is inadequate to reflect some characteristics of real-world applications (e.g., the change in dimensionality of the Pareto front due to the inclusion of constraints, a large number of constraints, and diverse geometries of unconstrained Pareto fronts). Therefore, we point out the need to further develop test problems with these real-world characteristics.