期刊:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ISSN:03029743 , 年:2018 . 卷:10941 LNCS 页码:509-519
语种:
English
原文链接:http://doi.org/10.1007/978-3-319-93815-8_48
会议名称:
9th International Conference on Swarm Intelligence, ICSI 2018
会议时间:
June 17, 2018 - June 22, 2018 会议地点:
Shanghai, China
- 摘要
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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.
© Springer International Publishing AG, part of Springer Nature 2018.
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关键词
Conflicting objectives - Differential Evolution - Multi objective evolutionary algorithms - Multi-objective optimization problem - Observational errors - Original signal - Sparse reconstruction - Speed of convergence
- 作者信息
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通讯作者:
Tian, Shujuan(sjtianwork@xtu.edu.cn)
作者机构:
[1]
College of Information Engineering, Xiangtan University, Xiangtan; 411105, China
[2]
National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha; 410082, China
