A self-adaptive embedded chaotic particle swarm optimization for parameters selection of W v -SVM

2011 ◽  
Vol 38 (1) ◽  
pp. 184-192 ◽  
Author(s):  
Qi Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Parameters Selection ◽  
Chaotic Particle Swarm Optimization ◽  
Selection Of ◽  
Self Adaptive

scholarly journals Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 140-149 ◽  
Author(s):  
Huang Dong ◽  
Gao Jian
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameter Selection ◽  
Support Vector ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Selection Of

Abstract This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and frequency of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.

Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy

2014 ◽  
Vol 1049-1050 ◽  
pp. 1654-1657
Author(s):  
Jie Liu ◽  
Xu Sheng Gan ◽  
Wen Ming Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Parameters Selection ◽  
Sharing Strategy ◽  
Selection Of

To optimize the parameters of LS-SVM effectively, an improved Particle Swarm Optimization (PSO) algorithm is proposed to select the optimal parameters combination. For the improvement of the precocity in PSO algorithm, an multi-particles sharing strategy is introduced in simple PSO algorithm to enhance the convergence. The simulation indicates that the proposed PSO algorithm has a better selection on LS-SVM parameters.

Study on Chaotic Particle Swarm Optimization Algorithm in Solution of Logistics Scheduling Problem

2013 ◽  
Vol 798-799 ◽  
pp. 720-727
Author(s):  
Yu Qiang Chen ◽  
Na Xin Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Simulation Analysis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Weighting Factor ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Self Adaptive

Study the basic theory and process of chaos particle swarm optimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical model of basic logistics scheduling to complete the simulation analysis experiments. Experiment results show that self-adaptive chaos particle swarm optimization algorithm is effective and feasible to solve the logistics scheduling model problem.

Sign in / Sign up

Export Citation Format

Share Document