Parallel social spider clustering algorithm for high dimensional datasets
Parallel social spider clustering algorithm for high dimensional datasets
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DOI：
10.1016/j.engappai.2016.08.013
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发表时间：
2016-11-01
影响因子：
8
通讯作者：
Nanda, Satyasai Jagannath
中科院分区：
文献类型：
--
作者：
Shukla, Urvashi Prakash;Nanda, Satyasai Jagannath
社交 - 旋转优化（SSO）是最近开发的群体智能之一。它的灵感来自生活在巨大殖民地中的蜘蛛的社会行为。在此手稿中，该算法的并行版本通过同时运行的蜘蛛（女性，占主导地位和非优势男性）的位置更新过程来制定并称为P-SSO。发现使用建议的P-SSO对基准高维数据集的群集分析的仿真研究在计算上比原始版本的SSO快了近10倍。与其他标准平行版本算法（如自适应平行粒子群优化（PPSO），实际编码平行遗传算法（RCPGA）和K-均值）的比较分析揭示了所提出方法的出色聚类精度。设计算法还在现实生活中测试，在现实生活中，多光谱图像分割被提出为聚类问题。这些图像取自NASA Landsat 8。它覆盖了在洪水条件下和之后获得的200公里钦奈的面积。这样做是为了分析洪水的严重性。在所有方法中，P-SSO的总体准确性最高。同样，在发现水淹没地区的情况下，生产商的准确性为76.89％，比K-均值好两倍。 （c）2016 Elsevier Ltd.保留所有权利。
The Social-Spider Optimization (SSO) is one of the recently developed swarm intelligence. It is inspired from the social behavior of spiders living in huge colonies. In this manuscript, a parallel version of this algorithm is formulated and termed as P-SSO by making a position update process of spiders (female, dominant and non-dominant male) running simultaneously. Simulation studies on cluster analysis of benchmark high-dimensional datasets using proposed P-SSO are found to be nearly 10 times computationally faster than the original version of SSO. Comparative analysis with other standard parallel version algorithms like Adaptive Parallel Particle Swarm Optimization (PPSO), Real Coded Parallel Genetic Algorithm (RCPGA) and K-means reveals the superior clustering accuracy of the proposed method. The designed algorithm is also tested on real life application where multi-spectral image segmentation is formulated as a clustering problem. The images are taken from NASA landsat 8. It covers an area of 200 km of Northwest Chennai obtained before and after flood conditions. This is done to analyze the flood severity. The overall accuracy of P-SSO is highest among all the methods. Also, in case of detection of water flooded areas the producer's accuracy is 76.89% which is two times better than K-means. (C) 2016 Elsevier Ltd. All rights reserved.