Method of Testing Point Optimizing Based on Multi-Echelon Comparison Hybrid Particle Swarm Algorithm

Zhi Qiang Wang; Yan Xu; Qing Yang

doi:10.4028/www.scientific.net/AMM.513-517.1935

Paper Titles

Research and Implementation of the Smart Home System Based on Internet of Things Environment
p.1915

The Numerical Analysis and Simulation of a Linearized Crank-Nicolson H¹-GMFEM for Nonlinear Coupled BBM Equations
p.1919

Automatic Mining and Processing Dormancy Data in the Database Management System for Small and Medium Enterprises
p.1927

Using Semantic Correlation of HowNet for Short Text Classification
p.1931

Method of Testing Point Optimizing Based on Multi-Echelon Comparison Hybrid Particle Swarm Algorithm
p.1935

Research on the Application of Computer Data Mining Technology in the Financial Risk Management
p.1940

The Study of the Requirement of Software Testability Based on Causal Analysis
p.1944

A Proxy Signature Based on the Difficulty of Solving Equations of Higher on Quaternion Ring
p.1951

Application of Informational Technology in Language Teaching
p.1955

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Method of Testing Point Optimizing Based on...

Method of Testing Point Optimizing Based on Multi-Echelon Comparison Hybrid Particle Swarm Algorithm

Abstract:

This paper introduces the multi-echelon comparison hybrid particle swarm optimization (PSO) algorithm and its processes, and describes its application in the testing point optimization, and the simulation analysis and comparison show that the algorithm improves the convergence speed of global search, and overcomes the shortcoming that fundamental particle swarm optimization algorithm is easy to fall into "premature" convergence and other shortcomings.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

1935-1939

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.1935

Citation:

Cite this paper

Online since:

February 2014

Authors:

Zhi Qiang Wang*, Yan Xu, Qing Yang

Keywords:

Genetic Algorithm (GA), Multi-Echelon Comparison, Particle Swarm Optimization (PSO) Algorithm, Testing Point Optimizing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Shi Y, eberhart R. A Modified Panicle Swarm Optimizer. Piscataway[C], NJ: (1998).

Google Scholar

[2] Liu Jianhua. swarm optimization and improvement of the basic theory[D]. Changsha: Central South University, (2009).

Google Scholar

[3] Zhang Liping. Particle swarm optimization theory and practice[D]. Hangzhou: Zhejiang University, (2005).

Google Scholar

[4] Zhang Liping, Yu HuanJun, Chen DeZhao, Hu Shangxu. Particle Swarm Optimization Analysis and Improvement[J]. Information and Control, (2004).

Google Scholar

[5] Jiang Ronghua. Research of electronic systems testability based on particle swarm[D]. Chengdu: University of Electronic Science and Technology, (2009).

Google Scholar

[6] Guo Mingming. Radar intelligent BIT status monitoring and Application optimization. Shijiazhuang: Ordnance Engineering College. (2012).

Google Scholar