@inproceedings{df90513d947a430f880cad9f2c136703,
title = "Enhanced NSGA based on adaptive crossover rate and reference points",
abstract = "The challenges of many-objective optimization are investigated; and one new algorithm, which is based on the NSGA-II, is proposed for multi-objective optimization in this paper. The reference points and an adaptable crossover rate are combined in the algorithm to improve the performance of NSGA-II. The performance of NSGA for optimizing the many objective search space is examined with and without the proposed algorithm through a constrained two-objective problem with up to 40 dimensions. Simulation results show that the proposed algorithm improves the performance of NSGA for the selected test problem in generations where a non-dominated set is not obtained by 39%.",
keywords = "convergence, diversity, inverted generational distance, optimization, reference points",
author = "Essiet, {Ima O.} and Yanxia Sun and Zenghui Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd International Conference on Robotics and Automation Engineering, ICRAE 2017 ; Conference date: 29-12-2017 Through 31-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICRAE.2017.8291398",
language = "English",
series = "2017 2nd International Conference on Robotics and Automation Engineering, ICRAE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "295--300",
booktitle = "2017 2nd International Conference on Robotics and Automation Engineering, ICRAE 2017",
address = "United States",
}