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Link to original content: https://doi.org/10.1007/s12559-016-9396-6
A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay | Cognitive Computation Skip to main content
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A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay

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Abstract

In this paper, the parameter identification problem of the lateral flow immunoassay (LFIA) devices is investigated via a new switching delayed particle swarm optimization (SDPSO) algorithm. By evaluating an evolutionary factor in each generation, the velocity of the particle can adaptively adjust the model according to a Markov chain in the proposed SDPSO method. During the iteration process, the SDPSO can adaptively select the inertia weight, acceleration coefficients, locally best particle pbest and globally best particle gbest in the swarm. It is worth highlighting that the pbest and the gbest can be randomly selected from the corresponding values in the previous iteration. That is, the delayed information of the pbest and the gbest can be exploited to update the particle’s velocity in current iteration according to the evolutionary states. The strategy can not only improve the global search but also enhance the possibility of eventually reaching the gbest. The superiority of the proposed SDPSO is evaluated on a series of unimodal and multimodal benchmark functions. Results demonstrate that the novel SDPSO algorithm outperforms some well-known PSO algorithms in aspects of global search and efficiency of convergence. Finally, the novel SDPSO is successfully exploited to estimate the unknown time-delay parameters of a class of nonlinear state-space LFIA model.

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Acknowledgments

This work was supported in part by the Royal Society of the U.K., the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of China under Grant 61403319, the Fujian Natural Science Foundation under Grant 2015J05131, and the Fujian Provincial Key Laboratory of Eco-Industrial Green Technology.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, Fujian, 361005, People’s Republic of China

    Nianyin Zeng & Hong Zhang

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang

  3. Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Zidong Wang & Fuad E. Alsaadi

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  1. Nianyin Zeng
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  2. Zidong Wang
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  4. Fuad E. Alsaadi
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Correspondence to Nianyin Zeng.

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Nianyin Zeng, Zidong Wang, Hong Zhang and Fuad E. Alsaadi declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human or animal subjects performed by the any of the authors.

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Zeng, N., Wang, Z., Zhang, H. et al. A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay. Cogn Comput 8, 143–152 (2016). https://doi.org/10.1007/s12559-016-9396-6

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