A feature extraction method of the particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization for Brillouin scattering spectra

In this paper, a hybrid algorithm called Group Search Particle Swarm Optimization (GSPSO) based on the Particle Swarm Optimization (PSO) and the Group Search Optimization (GSO) is proposed, in which the PSO model and the GSO model are used in turn. The GSPSO combines the advantages of the two algorithms, one is the high computing speed of the PSO and the other is the good performance of the GSO for high-dimension problems. In the GSPSO, the PSO model is used to find a good local search space, which the global optimization point is contained with high probability, and the GSO model is used to search in the local search space, and the rangers are used to revise the local search space at the same time. A mutual rescue method is also proposed for switching the two models. Moreover, a mechanism of weeding out the weak members is also established to increase the diversity of particles. Four benchmark functions are used to evaluate the performance of the novel algorithm. The results show that the GSPSO has better convergence accuracy for high-dimension problems and most low-dimension problems compared to other four algorithms.

A hybrid algorithm based on particle swarm optimization and group search optimization

A model is presented to model hydrographical flow, which we apply to flood forecasting in the River Severn catchment area. The approach uses Particle Swarm Optimization (PSO), a swarm computation heuristic, to produce a predictive model of hydrographical flow. Hydrological flow data from 1980 to 1990 are considered, comprising the daily average flow through the River Severn and its tributaries. PSO models are developed from each year of data and are applied to predict flow in the other 10 years; model performance is shown to be largely independent of the training year, suggesting the catchment system is stable and the approach is robust. Importantly, and in contrast to most of the existing alternatives, flow is derived from data measurements taken 2 days previously, as demanded for early-warning flood prediction. The cross-validated model for prediction of extreme (Q95) events R2 = 0.96, significantly improving upon multiple linear regression R2 = 0.93, the best performing of current existing methods.

/pdf/hydrographical-flow-modelling-of-the-river-severn-using-2zij366icb.pdf

Hydrographical Flow Modelling of the River Severn Using Particle Swarm Optimization

Support vector machine is a machine learning method. It takes on the good generalization ability and prediction accuracy. But the parameters of SVM model seriously affect the generalization ability and prediction accuracy of SVM model on the great extent. So an improved particle swarm optimization (PSO) algorithm based on chaotic search is introduced into the SVM model in propose a novel data classification (AMPSVM) method for processing the complex data. The first, the ergodicity, stochastic property, and regularity of chaos is used to chaotically search the current best individual, which randomly replaces the selected individual in the population in order to speed up evolution, improve the searching ability, convergence speed and accuracy. Then the improved PSO algorithm is used to select and optimize the parameters of the SVM (AMPSVM) model in order to improve the learning performance and generalization ability of the SVM model. In order to verify the effectiveness of the AMPSVM method, UCI data is selected in here. The experiment results show that the proposed AMPSVM method takes on the strong generalization ability, best sensitivity and higher classification accuracy.

http://www.sersc.org/journals/IJDTA/vol8_no4/28.pdf

A Novel Classification Method based on Improved SVM and its Application

Due to the low computational precision, local optimal solution and slow convergence speed of particle swarm optimization (PSO) algorithm, an improved PSO (SAQPSO) algorithm based on simulated annealing (SA) and quantum theory is proposed in this paper. The first, quantum theory is used to change the updating mode of the particles in order to improve the search speed and the convergence precision, and guarantee the simplification and effectiveness. Then the SA with probability and local search ability is introduced into quantum PSO (QPSO) in order to keep the diversity of the population, avoid falling into local optimum and enhance the global search ability. The SAQPSO algorithm keeps the characteristics of the simple and easy implementation, improves the global optimization ability and the convergence speed and the accuracy. Finally, some benchmark functions are used to prove the validity of the proposed SAQPSO algorithm. The computational results show that the proposed SAQPSO algorithm takes on the fast convergence speed, the better robustness and global search ability.

http://www.sersc.org/journals/IJDTA/vol8_no6/17.pdf

An Improved PSO Algorithm Based on SA and Quantum Theory and Its Application

当优化复杂高度维的功能时，基本粒子群优化(bPSO ) 的缺乏是它的无所不在的早熟和它的无能寻求全球最佳的答案。克服如此的缺乏， chaos-PSO (COSPSO ) 算法被介绍混乱优化机制和全球粒子停滞骚乱策略进 bPSO 建立。在改进算法，混乱运动被采用让粒子的起始的运动轨道代替以前的随机的运动，并且混乱因素被用来指导粒子的路径。当全球粒子是停滞的时，骚乱策略被用来把粒子放在运动。五基准优化被介绍测试 COSPSO，并且他们证明 COSPSO 能显著地在优化复杂功能改进效率。最后，在精明的设计洪水自记水位计的 COSPSO 的案例研究表明了改进算法的适用性。

Improved PSO algorithm based on chaos theory and its application to design flood hydrograph

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Improved PSO algorithm based on chaos theory and its application to design flood hydrograph