An adjoint feature-selection-based evolutionary algorithm for sparse large-scale multiobjective optimization
Abstract Sparse large-scale multiobjective optimization problems (sparse LSMOPs) are characterized by an enormous number of decision variables, and their Pareto optimal solutions consist of a majority of decision variables with zero values. This property of sparse LSMOPs presents a great challenge i...
Saved in:
Main Authors: | Panpan Zhang, Hang Yin, Ye Tian, Xingyi Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2025-01-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01752-1 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Cardinality-constrained structured data-fitting problems
by: Fan, Zhenan, et al.
Published: (2024-05-01) -
Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression
by: Anna Islamiyati, et al.
Published: (2025-06-01) -
Sparse Regularization With Reverse Sorted Sum of Squares via an Unrolled Difference-of-Convex Approach
by: Takayuki Sasaki, et al.
Published: (2025-01-01) -
Geodetic data inversion to estimate a strain-rate field by introducing sparse modeling
by: Yohei Nozue, et al.
Published: (2025-02-01) -
CR-DEQ-SAR: A Deep Equilibrium Sparse SAR Imaging Method for Compound Regularization
by: Guoru Zhou, et al.
Published: (2025-01-01)