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Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy

机译：2D Kapur熵多阈值图像分割的混沌随机备用蚁群优化

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摘要

Although the continuous version of ant colony optimizer (ACOR) has been successfully applied to various problems, there is room to boost its stability and improve convergence speed and precision. In addition, it is prone to stagnation, which means it cannot step out of the local optimum (LO). To effectively mitigate these concerns, an improved method using a random spare strategy and chaotic intensification strategy is proposed. Also, its selection mechanism is enhanced in our research. Among the new components, the convergence speed is mainly boosted by using a random spare approach. To effectively augment the ability to step out of LO and to refine the convergence accuracy, the chaotic intensification strategy and improved selection mechanism are applied to ACOR. To better verify the effectiveness of the proposed method, a series of comparative experiments are conducted by using 30 benchmark functions. According to all experimental results, it is evident that the convergence rapidity and accuracy of the proposed method is better than other peers. In addition, it is observed that the capability of enhanced RCACO is more reliable than other techniques in stepping out of LO. Furthermore, an excellent multi-threshold image segmentation method is proposed in this paper. On this basis, image segmentation experiments at low threshold levels and high threshold levels are also respectively carried out. The experimental results also adequately disclose that the segmentation results of RCACO for both multi-threshold image segmentation at a low threshold level and high threshold level, are even more satisfactory compared to other studied algorithms. An online homepage supports this research for access to sharable codes, any question and info about this research at https://aliasgharheidari.com. (C) 2020 Elsevier B.V. All rights reserved.
机译：虽然蚁群优化器（ACOR）的连续版本已成功应用于各种问题，但有空间可以提高其稳定性，提高收敛速度和精度。此外，它易于停滞，这意味着它不能退出局部最佳（LO）。为了有效减轻这些问题，提出了一种使用随机备用策略和混沌强化策略的改进方法。此外，在我们的研究中得到了选择机制。在新的组件中，收敛速度主要通过使用随机备用方法来提升。为了有效地增强了走出LO并优化收敛准确性的能力，混沌强化策略和改进的选择机制应用于锐频。为了更好地验证所提出的方法的有效性，通过使用30个基准函数进行一系列比较实验。根据所有实验结果，显然提出的方法的收敛速度和准确性优于其他同行。另外，观察到，增强型RCACO的能力比踩出LO踩出的其他技术更可靠。此外，本文提出了优异的多阈值图像分割方法。在此基础上，还分别进行低阈值水平和高阈值水平的图像分割实验。实验结果还充分公开了与其他研究算法相比，在低阈值水平和高阈值水平下，RCACO的分割结果甚至更加令人满意。在线主页支持此研究，以便访问可共享代码，在HTTPS://aliasgharheidar.com上获取有关本研究的任何问题和信息。 （c）2020 Elsevier B.v.保留所有权利。

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  • 来源
    《Knowledge-Based Systems》 |2021年第15期|106510.1-106510.61|共61页
  • 作者

    Zhao Dong; Liu Lei; Yu Fanhua; Heidari Ali Asghar; Wang Mingjing; Liang Guoxi; Muhammad Khan; Chen Huiling;

  • 作者单位

    Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China;

    Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China;

    Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China;

    Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran|Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore;

    Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam;

    Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China;

    Sejong Univ Dept Software Seoul 143747 South Korea;

    Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Ant colony optimization; Multi-threshold image segmentation; Metaheuristic; Kapur's entropy; Image;

    机译：蚁群优化;多阈值图像分割;成群质训练;Kapur的熵;图像;

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