A local search enhanced differential evolutionary algorithm for sparse recovery

Lin, Qiuzhen; Hu, Bishan; Tang, Ya Lian; Zhang, Leo Yu; Chen, Jianyong; Wang, Xiaomin; Ming, Zhong

doi:10.1016/j.asoc.2017.03.034

Cited by 14 publications

(2 citation statements)

References 51 publications

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“…Besides these standard heuristic algorithms, their improved versions (Zhong et al 2016a;Lin et al 2017;Yang et al 2017;Chen et al 2018;Huang et al 2019;Singh & Deep 2019;Zandevakili et al 2019) are also widely studied, such as the adaptive particle swarm optimization (APSO) algorithm (Zhang et al 2014), the modified artificial bee colony (MABC) algorithm (Gao & Liu 2012) and the self-adaptive differential evolution (SaDE) (Coelho et al 2013) algorithm. Compared with the standard algorithms, the improved versions enhance their performances in some aspects.…”

Section: Introductionmentioning

confidence: 99%

The feedback artificial tree (FAT) algorithm

2020

Soft Comput

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Inspired by the transport of organic matters and the update theories of branches, the artificial tree (AT) algorithm was proposed recently. This work presents an improved version of AT algorithm that is called the feedback artificial tree (FAT) algorithm. In FAT, besides the transfer of organic matters, the feedback mechanism of moistures is introduced.Meanwhile, the self-propagating operator and dispersive propagation operator are also put forward. Some typical benchmark problems are applied to test the performance of FAT. The experimental results have clearly demonstrated the higher performance of FAT compared with AT over the tested set of problems. In addition, some well-known heuristic algorithms and their improved algorithms are also applied to validate the performance of FAT, and the computational results of FAT listed in this study are the best among these algorithms. In addition, sensitive analyses on the specific parameters of FAT algorithm are carried out, and the performance of FAT is validated.

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Section: Introductionmentioning

confidence: 99%

The feedback artificial tree (FAT) algorithm

2020

Soft Comput

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“…In the compressive sensing theory [32][33][34], there mainly exist two sparse signal recovery methods: the orthogonal matching pursuit algorithms and the basis pursuit algorithms. The orthogonal matching pursuit (OMP) algorithm is a kind of greedy parameter recovering method [35][36][37], it selects the best fitting column of the measurement matrix and the corresponding sparse signal in each selected step.…”

Section: Introductionmentioning

confidence: 99%

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

2018

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Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system. The idea is, by the filtering technique and the instrumental variable method, to transform the Hammerstein system into a simple form with a separated nonlinear expression and to parameterize the system into an autoregressive model, then to perform an instrumental variable-based orthogonal matching pursuit (IV-OMP) identification method for the Hammerstein system. Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency.

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