A comparative study on constraint handling techniques of NSGAII

Abstract

Real-world optimization problems are bounded with constraints, most of the time, a number of constraints are enforced on the problems. Moreover, conflicting objectives are found in most real-world optimization problems. Therefore, most realworld problems become constrained multi-objective optimization problems. Multi-objective optimization problems (MOOP) are mostly solved by using multi-objective evolutionary algorithms (MOEA). Therefore, a number of constraint handling techniques are proposed for MOEA. On the other hand, non-dominated sorting genetic algorithm-II (NSGAII) is the most frequently used algorithm when solving a MOOP. In this paper, a comparison is made among the three selected proposed constraint handling techniques that are easily adopted into NSGAII. The test is conducted on six different benchmark problems. The constrained dominance principle technique has achieved better results over the self-adaptive penalty and the adaptive trade-off model.