A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Wang, Chuang; Zhang, Yong; Song, Jinbo; Liu, Qingqiang; Dong, Hongli
• Is Part Of: Systems science & control engineering. Volume 7:Issue 1(2019)
• Page Start: 75
• Page End: 88
• Publication Date: 2019-01-01
• Subjects: Oil pipeline leak detection -- classification -- particle swarm optimization -- support vector machine -- saturation -- mixed time-delays; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2019.1573386
• Languages: English
• ISSNs: 2164-2583

Abstract

ABSTRACT: In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of the traditional support vector machine (SVM) approaches with applications in analyzing data of oil pipeline leak detection. In the proposed saturated and mix-delayed particle swarm optimization (SMDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, time-varying time-delays and distributed time-delays are introduced in the velocity updating model to respectively reflect the history of previous personal and global optimum particles. The introduction of saturation constraint ensures that the particles will convergence in case that the velocity of the particles is too large. Eight well-known benchmark functions are employed to evaluate the proposed SMDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the developed framework SMDPSO-based SVM algorithm is exploited in the problem of oil pipeline leak detection. Experiment results demonstrate that the SMDPSO-based SVM method is superior over other well-known classification algorithms.