This Paper provides a new approach to handle constrained multi objective problems using a modification to the fast elitist multi objective Genetic Algorithm, NSGA II (Non-dominated Sorting Genetic Algorithm II). The modification overcomes shortcomings of some previous approaches to handle constrained multi objective problems like Penalty Function Approach, Jimenez-Verdegay-Gomez-Skarmeta's approach, and Ray-Tai-Seow's Method. Comparative studies are conducted using a group of quality indices [2,19] to compare

This Paper provides a new approach to handle constrained multi objective problems using a modification to the fast elitist multi objective Genetic Algorithm, NSGA II (Non-dominated Sorting Genetic Algorithm II). The modification overcomes shortcomings of some previous approaches to handle constrained multi objective problems like Penalty Function Approach, Jimenez-Verdegay-Gomez-Skarmeta's approach, and Ray-Tai-Seow's Method. Comparative studies are conducted using a group of quality indices [2,19] to compare among three different algorithms:  regular NSGA, NSGA-II with penalty function and M-NSGA-II with constraint handling techniques. Results show that the new modification is the best among the three approaches.  Due to limited space, only recent studies on the subject of multi objective optimization are reviewed for brevity.

three different algorithms:  regular NSGA, NSGA-II with penalty function and M-NSGA-II with constraint handling techniques. Results show that the new modification is the best among the three approaches.  Due to limited space, only recent studies on the subject of multi objective optimization are reviewed for brevity.