TY - GEN
T1 - MOEA/D with iterative thresholding algorithm for sparse optimization problems
AU - Li, Hui
AU - Su, Xiaolei
AU - Xu, Zongben
AU - Zhang, Qingfu
PY - 2012
Y1 - 2012
N2 - Currently, a majority of existing algorithms for sparse optimization problems are based on regularization framework. The main goal of these algorithms is to recover a sparse solution with k non-zero components(called k-sparse). In fact, the sparse optimization problem can also be regarded as a multi-objective optimization problem, which considers the minimization of two objectives (i.e., loss term and penalty term). In this paper, we proposed a revised version of MOEA/D based on iterative thresholding algorithm for sparse optimization. It only aims at finding a local part of trade-off solutions, which should include the k-sparse solution. Some experiments were conducted to verify the effectiveness of MOEA/D for sparse signal recovery in compressive sensing. Our experimental results showed that MOEA/D is capable of identifying the sparsity degree without prior sparsity information. © 2012 Springer-Verlag.
AB - Currently, a majority of existing algorithms for sparse optimization problems are based on regularization framework. The main goal of these algorithms is to recover a sparse solution with k non-zero components(called k-sparse). In fact, the sparse optimization problem can also be regarded as a multi-objective optimization problem, which considers the minimization of two objectives (i.e., loss term and penalty term). In this paper, we proposed a revised version of MOEA/D based on iterative thresholding algorithm for sparse optimization. It only aims at finding a local part of trade-off solutions, which should include the k-sparse solution. Some experiments were conducted to verify the effectiveness of MOEA/D for sparse signal recovery in compressive sensing. Our experimental results showed that MOEA/D is capable of identifying the sparsity degree without prior sparsity information. © 2012 Springer-Verlag.
KW - evolutionary algorithm
KW - hard/ half thresholding algorithm
KW - multi-objective optimization
KW - sparse optimization
UR - https://www.scopus.com/pages/publications/84866433638
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84866433638&origin=recordpage
U2 - 10.1007/978-3-642-32964-7_10
DO - 10.1007/978-3-642-32964-7_10
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783642329630
VL - 7492 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 93
EP - 101
BT - Parallel Problem Solving from Nature, PPSN XII
PB - Springer Verlag
T2 - 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012
Y2 - 1 September 2012 through 5 September 2012
ER -