Forecasting agriculture water consumption based on PSO and SVM

Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing capability of SVM and parameters optimization capability of PSO. Firstly, P-SVM model uses PSO algorithm to calculate the best parameters of SVM, and then it adopts the optimized SVM model to predict software defect. P-SVM model and other three different prediction models are used to predict the software defects in JM1 data set as an experiment, the results show that P-SVM model has a higher prediction accuracy than BP Neural Network model, SVM model, GA-SVM model.

The main objective of this paper is to accurately estimate the fault location in a series compensated transmission line using integration of discrete wavelet transform, particle swarm optimization (PSO) and support vector machine based algorithm. Estimation of accurate fault location will lead to quicker restoration of the supply. Instantaneous values of faulty current, voltage and power signals are available at the relay location. Using 10-level Discrete Wavelet Transform (DWT), the available signals are decomposed and thereafter the statistical features are obtained from the decomposed signals. Forward feature selection algorithm is used to select the best feature set. Selected features are then applied to the support vector machine (SVM) for estimating the fault distance. PSO algorithm is used to select the best SVM parameter by global searching technique. Proposed fault locator has been trained and tested for different fault scenarios (fault resistance and phase difference). The test results demonstrate that the adopted technique is a reliable method for estimating fault locations accurately.

Support Vector Machine (SVM) has become a popular method in machine learning in recent years. SVM is widely used as a tool in many classifiction areas. The selection optimum values of the parameters (model selection) for SVM is an important step in SVM design. This paper presents the results of research and testing method Particle Swarm Optimization (PSO) in selecting the parameters for SVM. Here we focus on the selection parameter γ of the RBF function and soft margin parameter C of SVM for classification problems. In this paper we propose a Particle Swarm Optimization variation model which combine the cognition-only model with the social-only model and use randomised low discrepancy sequences for initialising particle swarms. We then evaluate the suggested strategies with a series of experiments on 6 benchmark datasets. Experimental results demonstrate the model we proposed better than grid search and full PSO-SVM model.

Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models.

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.

The parameters of support vector machine (SVM) are crucial to the model's classification performance. Aiming at the randomicity of selecting the parameters in SVM, this paper constructed a PSO-SVM model by using particle swarm optimization (PSO) to search the parameters of SVM. The model was used for personal credit scoring in commercial banks and particles' fitness function was used to control the type II error which costs huger loss to commercial banks. Compared with BP NN, the application results indicate that PSO-SVM gets higher classification accuracy with lower type II error rate and the model shows stronger robustness, which presents more applicable for commercial banks to control personal credit risks.

Feedback analysis of tunnel construction using a hybrid arithmetic based on Support Vector Machine and Particle Swarm Optimisation

Inspired by the swarm intelligence in self- organizing behavior of real Particle Swarm Optimization various Particle Swarm Optimization algorithms were proposed recently for many research fields in data mining such as clustering Compared with the previous clustering approaches such as K-means the main advantage of Particle Swarm Optimization based clustering algorithms is that no additional information is needed such as the initial partitioning of the data or the number of clusters In this paper, we discuss the clustering analysis way by a combination of advantages of particle swarm optimization in the clustering, since Particle Particle Swarm Optimization has the good global searching quickly. Firstly, the center and number of clustering are determined by using the Particle Swarm Optimization, and then the above clustering results are optimized by the K-means algorithm combining with the optimization algorithm. The simulated experiments show that the combining algorithm is obviously superior to some common clustering algorithms since it has obvious advantage in optimization capacity, more efficient and more robust than previous research such as the classical K-means clustering algorithm.

This abstract presents the influence of meteorological and hydrological droughts on groundwater drought conditions through the calculation and analysis of four widely used drought indices: Standardized Precipitation Index (SPI), Reconnaissance Drought index (RDI), Streamflow Drought Index (SDI), and Groundwater Resource Index (GRI). These indices were computed using long-term observed data including precipitation, temperature, discharge, and groundwater levels to capture the temporal and spatial variability of drought events in the Chaliyar River Basin. By analyzing the spatial and temporal variability of groundwater levels and discharge rates and measuring the impact of future drought events under climate change conditions. The research utilizes historical data to understand the relationship between drought and groundwater availability, informing sustainable water management strategies. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. It begins with the collection of groundwater storage data, discharge data, and climate data, alongside outputs from global climate models (GCMs). The data is processed with a Gamma Test to determine the best combination of meteorological and hydrological drought indices as input variables. A Support Vector Machine (SVM) for groundwater simulation, which is optimized through Particle Swarm Optimization (PSO) to select the best SVM parameters. The developed SVM model undergoes validation using 5-fold cross- validation to ensure accuracy. Climate data downscaled for future scenarios, specifically RCP4.5 and RCP8.5, is then utilized to project drought events for the period 2022-2050. This enables an assessment of anticipated changes in groundwater storage and its behavior during future drought events under different climate conditions. This study investigates the interconnected effects of meteorological and hydrological droughts on groundwater storage in a changing climate. Using a combination of observational data, hydrological modelling, and climate projections, it assesses the impacts of drought on groundwater recharge and storage.

A genetic algorithm–support vector machine method with parameter optimization for selecting the tag SNPs

This paper presents a novel fault diagnosis model for oil-immersed power transformers based on dissolved gas analysis. The model is rooted on the theories of rough set and support vector machine. A fitness function based on attribute dependence is developed to identify fault features to improve classification accuracy of transformer fault samples by using Genetic Algorithm. To get improved classification performance, grid search, genetic algorithm and particle swarm optimization are applied to search parameters of support vector machine. Compared with modified Rogers and back propagation neural network, the superiority of the established model is verified.

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs; in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm.

The success of any Intrusion Detection Systems (IDSs) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features is very important topic for an intrusion detection problem. Support vector machine (SVM) has been employed to provide potential solutions for the IDSs problem. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Artificial Bee Colony algorithm (ABC) is an optimization method, which is not only has strong global search capability, but also is very easy to implement. Thus, the proposed ABC-SVM model is applied to determine free parameters of SVM and feature selection at building intrusion detection system. The standard ABC is used to determine free parameters of support vector machine and the binary ABC is to obtain the optimum feature selection for IDSs from KDD Cup 99 data set. The experimental results indicate that the ABC-SVM method can achieve higher accuracy rate than Particle swarm optimization (PSO) and GA-SVM algorithms in the same time.

A model integrating Particle Swarm Optimization (PSO) and support vector machines (SVM) is presented to forecast short-term load of electric power systems in this paper. PSO is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSO searches the solution space intelligently and finds out the best one. The PSO-SVM method proposed in this paper is based on the global optimization of PSO and local accurate searching of SVM. Practical example results indicate that the application of the PSO-SVM method to short term load forecasting of power systems is feasible and effective. And to prove the effectiveness of the model, other existing methods are used to compare with the result of SVM. The results show that the model is effective and highly accurate in the forecasting of short-term power load.

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.