MOEA/D with iterative thresholding algorithm for sparse optimization problems

  • Authors:

  • Hui Li;Xiaolei Su;Zongben Xu;Qingfu Zhang

  • Affiliations:

  • Institute for Information and System Sciences & Ministry of Education, Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Institute for Information and System Sciences & Ministry of Education, Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Institute for Information and System Sciences & Ministry of Education, Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, Shaanxi, China;School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK

  • Venue:
  • PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
  • Year:
  • 2012

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Abstract

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.