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Study neighborhood field optimization algorithm on nonlinear sorptive barrier design problems

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Abstract

Using a simulation-based approach, sorptive barrier design can be expressed as a nonlinear and mixed-integer optimization problem, and metaheuristic searching algorithms are suitable optimization methods to find optimal configurations of design. A recently proposed neighborhood field optimization (NFO) algorithm is applied to deal with the sorptive barrier design problem. NFO is originally proposed for continuous optimization problems, then, it is extended to binary NFO for discontinuous optimization problems. In this paper, integer NFO (INFO) is proposed by using forward and backward transformations. For the sorptive barrier design, NFO variants are compared with genetic algorithms and the best performer reported previously. Based on statistical analysis, NFO variants show better performance than GA variants in terms of accuracy and convergence speed, and INFO improves the best known results on all test instances. It can be concluded that the proposed INFO is suitable for the sorptive barrier design with significant performance improvement.

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Authors and Affiliations

  1. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xin Zhang

  2. School of Automation, Chongqing University, Chongqing, China

    Zhou Wu

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  1. Xin Zhang
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  2. Zhou Wu
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Correspondence to Zhou Wu.

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Zhang, X., Wu, Z. Study neighborhood field optimization algorithm on nonlinear sorptive barrier design problems. Neural Comput & Applic 28, 783–795 (2017). https://doi.org/10.1007/s00521-015-2106-6

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  • DOI: https://doi.org/10.1007/s00521-015-2106-6

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