2023

Conférence

International Conference on Control Decision and Information Technology Codit’9, Rome, 1565-1570

Multi-Objective Evolutionary Algorithms (MOEAs) is actually one of the most attractive and active research field in computer science. Significant research has been conducted in handling complex multi-objective optimization problems within this research area. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) has garnered significant attention in various domains, emphasizing its specific popularity. However, the complexity of this algorithm is found to be O(MN2) with M objectives and N solutions, which is considered computationally demanding. In this paper, we are proposing a new variant of NSGA-II termed (Efficient-NSGA-II) based on our recently proposed quick non-dominated sorting algorithm with quasi-linear average time complexity; thereby making the NSGA-II algorithm efficient from a computational cost viewpoint. Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. Moreover, comparisons results against other multi-objective algorithms on a variety of benchmark problems show the effectiveness and the efficiency of this multi-objective version

@inproceedings{chaabani2023efficient,
  title={An efficient non-dominated sorting genetic algorithm for multi-objective optimization},
  author={Chaabani, Abir and Karaja, Mouna and Said, Lamjed Ben},
  booktitle={2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)},
  pages={1565--1570},
  year={2023},
  organization={IEEE}
}