An Improved NSGA-II Algorithm Based on Crowding Distance Elimination Strategy

DOI

10.2991/ijcis.d.190328.001How to use a DOI?

Keywords

NSGA-II; Crowding distance elimination; Diversity; Convergence

Abstract

Aiming at the diversity of Nondominated Sorting Genetic Algorithm II (NSGA-II) in screening out nondominated solutions, a crowding distance elimination (CDE) method is proposed. Firstly, the crowding distance is calculated in the same level of nondominated solutions, and the solution of minimum crowding distance is eliminated; secondly, the crowding distance of residual solutions is recalculated, and the solution of minimum crowding distance is also eliminated. Repeat the above process, and stop the cycle when the nondominated solutions reaches the set number. In order to verify the effectiveness of the algorithm, experiments are carried out with the representative test functions: ZDT1, ZDT2, and ZDT3. The comparative experiments of NSGA-II, σ-Multi-objective particle swarm optimization algorithm (MOPSO), Non dominated sorting particle swarm optimization algorithm (NSPSO), and CDE were carried out respectively. By analyzing the diversity and convergence of the four algorithms, the strategy of nondominant solutions selection based on CDE has better performance.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Junhui Liu
AU  - Xindu Chen
PY  - 2019
DA  - 2019/04/12
TI  - An Improved NSGA-II Algorithm Based on Crowding Distance Elimination Strategy
JO  - International Journal of Computational Intelligence Systems
SP  - 513
EP  - 518
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190328.001
DO  - 10.2991/ijcis.d.190328.001
ID  - Liu2019
ER  -