...
首页> 外文期刊>Swarm and Evolutionary Computation >Quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for gray level image thresholding
【24h】

Quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for gray level image thresholding

机译:基于混沌映射模型的量子启发遗传算法和粒子群优化算法在灰度图像阈值检测中的应用

获取原文
获取原文并翻译 | 示例
           

摘要

In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of gray-level images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter.
机译:本文采用两种元启发式技术介绍了两种新的量子启发式元启发式方法,即量子启发式遗传算法和用于双层阈值的量子启发式粒子群算法。所提出的方法使用Otsu方法,Ramesh方法,Li方法,Shanbag方法以及相关系数作为评估函数来确定灰度图像的最佳阈值。他们利用量子计算的琐碎概念,例如量子位和状态叠加。这些特性有助于展现并行性的特征,而并行性又利用了量子力学系统的时间离散性。所提出的方法已经与它们的经典方法进行了比较,后来又与Han等人提出的量子进化算法(QEA)进行了比较。以评估所有参与算法的三个测试图像的性能。报告了具有相应适应度值的最佳阈值,适应度标准偏差,最后报告了每种方法对每种测试图像的计算时间。结果证明,与传统方法相比,该方法具有较高的时间效率。 Han等人提出的与量子进化算法(QEA)进行的方法的另一项比较研究。也揭示了后者的弱点。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号