Electricity Load Forecasting Based on Adaptive Quantum-Behaved Particle Swarm Optimization and Support Vector Machines on Global Level

2008 ◽  
Author(s):  
Jingmin Wang ◽  
Zejian Liu ◽  
Pan Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Global Level ◽  
Swarm Optimization ◽  
Vector Machines ◽  
Electricity Load ◽  
Electricity Load Forecasting

Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Weilin Luo ◽  
C. Guedes Soares ◽  
Zaojian Zou
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Difference Method ◽  
Particle Swarm ◽  
Support Vector ◽  
Ship Maneuvering ◽  
Swarm Optimization ◽  
Hydrodynamic Derivatives ◽  
Vector Machines ◽  
Maneuvering Motion

Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

2012 ◽  
Vol 57 (5) ◽  
Author(s):  
Mohammad Reza Daliri
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Particle Swarm ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Binary Particle Swarm Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

Optimizing parameters of support vector machines using team-search-based particle swarm optimization

2015 ◽  
Vol 32 (5) ◽  
pp. 1194-1213 ◽  
Author(s):  
Long Zhang ◽  
Jianhua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Search Strategy ◽  
Cross Validation ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type ◽  
Vector Machines

Purpose – It is greatly important to select the parameters for support vector machines (SVM), which is usually determined by cross-validation. However, the cross-validation is very time-consuming and complicated to create good parameters for SVM. The parameter tuning issue can be solved in the optimization framework. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors propose a novel variant of particle swarm optimization (PSO) for the selection of parameters in SVM. The proposed algorithm is denoted as PSO-TS (PSO algorithm with team-search strategy), which is with team-based local search strategy and dynamic inertia factor. The ultimate design purpose of the strategy is to realize that the algorithm can be suitable for different problems with good balance between exploration and exploitation and efficiently control the inertia of the flight. In PSO-TS, the particles accomplish the assigned tasks according to different topology and detailedly search the achieved and potential regions. The authors also theoretically analyze the behavior of PSO-TS and demonstrate they can share the different information from their neighbors to maintain diversity for efficient search. Findings – The validation of PSO-TS is conducted over a widely used benchmark functions and applied to tuning the parameters of SVM. The experimental results demonstrate that the proposed algorithm can tune the parameters of SVM efficiently. Originality/value – The developed method is original.

scholarly journals MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI

2019 ◽  
Vol 3 (2) ◽  
pp. 77
Author(s):  
Herlina Herlina ◽  
Ahmad Ridho’i ◽  
Anggie Erma Yunita ◽  
Mega Puja Azhari ◽  
Ade Reynaldi Saputra
Keyword(s):  
Logistic Regression ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Particle Swarm ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Swarm Optimization ◽  
Vector Machines

Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.

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