Study of the ternary correlation quantum-behaved PSO algorithm

Tao WU; Xi CHEN; Yu-song YAN

doi:10.11959/j.issn.1000-436x.2015076

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Study of the ternary correlation quantum-behaved PSO algorithm

Academic paper | 更新时间：2024-06-05

- Study of the ternary correlation quantum-behaved PSO algorithm
- Journal of Communications Vol. 36, Issue 3, Pages: 208-215(2015)
- 作者机构：
  
  1. 成都信息工程学院计算机学院，四川成都 610225
  2. 西南民族大学计算机科学与技术学院，四川成都 610041
  3. 西南交通大学计算机科学与技术学院，四川成都 610031
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61104175);Sichuan Province Soft Science Research Project(2012ZR0022);Sichuan Province Science Support Project(2012GZX0090)
- DOI：10.11959/j.issn.1000-436x.2015076
  CLC： TP391
- Online First：2015-03，
  
  Published：25 March 2015
- 稿件说明：
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Tao WU, Xi CHEN, Yu-song YAN. Study of the ternary correlation quantum-behaved PSO algorithm[J]. Journal of Communications, 2015, 36(3): 208-215.
DOI：

Tao WU, Xi CHEN, Yu-song YAN. Study of the ternary correlation quantum-behaved PSO algorithm[J]. Journal of Communications, 2015, 36(3): 208-215. DOI： 10.11959/j.issn.1000-436x.2015076.

摘要

为了提高QPSO算法的收敛性能，在对随机因子进行分析的基础上提出了三元相关性QPSO(TC-QPSO

ternary correlation QPSO)算法。该算法使用正态Copula函数建立了粒子对自身经验信息、群体共享信息以及粒子当前位置与群体平均最好位置的距离信息之间的内在认知和联系，并利用Cholesky平方根公式给出了三元相关因子的生成方法。对测试函数的仿真结果证明，当三元相关因子u与r1或r2之间存在负线性相关关系时，TC-QPSO算法可以获得比标准QPSO算法更好的优化性能。

Abstract

In order to more effectively utilize existing information and improve QPSO's (quantum-behaved particle swarm optimization) convergence performance

the ternary correlation QPSO (TC-QPSO) algorithm was proposed based on the analysis of the random factors in location formula. The novel algorithm changed the information independent ran-dom processing method of standard QPSO and established internal relations during particles' own experience information

group sharing information and the distance from the particles' current location to the population mean best position using normal copula functions.Then

the method of generating ternary correlation factors was given by using the Cholesky square root formula. The simulation results of the test functions showed that TC-QPSO algorithm outperforms the stan-dard QPSO algorithm in terms of optimization results

given that the negative linear correlation exists betweenu and r1 or u andr2.

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references

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