Xu Yang

In large-scale multi-objective optimization, as the decision space's dimensionality increases, evolutionary algorithms can easily fall into an optimal local state. Therefore, how to prevent the algorithm from falling into a local optimum and quickly converge to the Pareto front is a particularly challenging problem. In order to solve the problem, this paper proposes a grid-based fuzzy evolution large-scale multi-objective optimization framework, which divides the entire evolution process into two main stages: fuzzy evolution and precise evolution.

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Subscribe now or login.

[1] Xu Yang, "GFE date", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/6g2x-vy43. Accessed: Dec. 06, 2024.
@data{6g2x-vy43-21,
doi = {10.21227/6g2x-vy43},
url = {http://dx.doi.org/10.21227/6g2x-vy43},
author = {Xu Yang },
publisher = {IEEE Dataport},
title = {GFE date},
year = {2021} }
TY - DATA
T1 - GFE date
AU - Xu Yang
PY - 2021
PB - IEEE Dataport
UR - 10.21227/6g2x-vy43
ER -
Xu Yang. (2021). GFE date. IEEE Dataport. http://dx.doi.org/10.21227/6g2x-vy43
Xu Yang, 2021. GFE date. Available at: http://dx.doi.org/10.21227/6g2x-vy43.
Xu Yang. (2021). "GFE date." Web.
1. Xu Yang. GFE date [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/6g2x-vy43
Xu Yang. "GFE date." doi: 10.21227/6g2x-vy43