Abstract
ABSTRACTIn 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.
Highlights
Oil, as an important strategic resource, is needed as energy and raw materials in daily life, industrial production and aerospace industry
The results show that the proposed saturated and mix-delayed particle swarm optimization (SMDPSO) algorithm is superior to other particle swarm optimization (PSO) algorithms in the mean, minimum and standard deviation of fitness values of functions (14)–(21)
A new PSO algorithm is proposed for the sake of improving the accuracy of the traditional support vector machine approaches with applications in analysing data on oil pipeline leak detection In the proposed saturated and mix-delayed particle swarm optimization algorithm, the introduction of time-delays reduces the possibility of falling into local optima, the introduction of saturated term ensures convergence and the addition of nonlinear inertial weight increases the diversity of particles, greatly enhancing the search ability of the algorithm
Summary
As an important strategic resource, is needed as energy and raw materials in daily life, industrial production and aerospace industry. With the rapid development of the economy, the demand for oil is increasing, and the problem of transportation has gradually attracted people’s attention. Pipelines have the characteristics of high pressure, flammable and explosive medium, and environmental sensitivity. Accidents of pipeline leakage are frequent due to corrosion of materials, natural disasters, third-party damage and other reasons. In the event of leakage of oil pipelines, it causes economic losses, and brings environmental pollution and major accidents such as destruction, casualties, etc. The safe operation of oil pipelines affects the production order, economic development, social stability and national energy security supply directly
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