Improved NSGA-II Algorithm for Optimization of Constrained Functions

DOI

10.2991/eame-18.2018.67How to use a DOI?

Keywords

multi-objective optimization; improved non-dominated sorting genetic algorithm; infeasible solutions; external save set

Abstract

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.

Copyright

© 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/).

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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  -