Improved NSGA-II Algorithm for Optimization of Constrained Functions
- 10.2991/eame-18.2018.67How to use a DOI?
- multi-objective optimization; improved non-dominated sorting genetic algorithm; infeasible solutions; external save set
In order to solve the constrained multi-objective optimization problem, an improved NSGA-II algorithm is proposed. On the basis of NSGA, the cross operation of the feasible and unfeasible solution is implemented in order to give full play to the role of the infeasible solution in the optimization process. In addition, the external preservation set is updated on the basis of the obtained dominant individual to preserve the optimal solution of the problem. The improved algorithm is applied to typical test functions and compared with NSGA-II. The experimental results show that the algorithm is superior.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yun Zhang AU - Bin Jiao PY - 2018/06 DA - 2018/06 TI - Improved NSGA-II Algorithm for Optimization of Constrained Functions BT - Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018) PB - Atlantis Press SP - 316 EP - 319 SN - 2352-5401 UR - https://doi.org/10.2991/eame-18.2018.67 DO - 10.2991/eame-18.2018.67 ID - Zhang2018/06 ER -