Name
Title | ||
|---|---|---|
A Decomposition-Based Multiobjective Evolutionary Algorithm for Sparse Reconstruction. |
Abstract | ||
|---|---|---|
Sparse reconstruction is an important method aiming at obtaining an approximation to an original signal from observed data. It can be deemed as a multiobjective optimization problem for the sparsity and the observational error terms, which are considered as two conflicting objectives in evolutionary algorithm. In this paper, a novel decomposition based multiobjective evolutionary algorithm is proposed to optimize the two objectives and reconstruct the original signal more exactly. In our algorithm, a sparse constraint specific differential evolution is designed to guarantee that the solution remains sparse in the next generation. In addition, a neighborhood-based local search approach is proposed to obtain better solutions and improve the speed of convergence. Therefore, a set of solutions is obtained efficiently and is able to closely approximate the original signal. |
Year | Venue | Field |
|---|---|---|
2018 | ICSI | Convergence (routing),Mathematical optimization,Evolutionary algorithm,Computer science,Differential evolution,Multi-objective optimization,Multiobjective optimization problem,Local search (optimization),Observational error |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhu Jiang | 1 | 44 | 8.16 |
| Muyao Cai | 2 | 0 | 0.34 |
| Shujuan Tian | 3 | 4 | 2.12 |
| Yanbing Xu | 4 | 0 | 0.34 |
| Tingrui Pei | 5 | 46 | 14.75 |