Journal Article10.1016/j.asoc.2023.110122
Multi-regularization sparse reconstruction based on multifactorial multiobjective optimization
Wenmin Han,Hao Li,Maoguo Gong +2 more
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TL;DR: In this paper , a multi-regularization based on multifactorial multiobjective optimization is proposed to solve the sparse reconstruction problem, where the sparsity and reconstruction error can be considered as two objectives.
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About: This article is published in Applied Soft Computing. The article was published on 01 Feb 2023. The article focuses on the topics: Computer science & Regularization (linguistics).
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Citations
Application of Multi-objective Optimization in 3D Image Reconstruction
Fengli Zhang
TL;DR: The research content of this paper aims to improve the quality of reconstruction results and provide more reliable technical support for practical applications, in the hopes of enriching the theoretical foundation of 3D image reconstruction as well as offering new technical approaches for practical applications.
A Survey of Subgraph Optimization for Expert Team Formation
Mahdis Saeedi,Hawre Hosseini,Hossein Fani +2 more
TL;DR: This survey provides a taxonomy and unifying overview of graph-based search-based approaches for Expert Team Formation, reviewing initial approaches, objective functions, and evaluation schemas, identifying shortfalls and proposing future directions for algorithmic improvement.
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Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.