Particle swarm optimization in regression analysis: A case study

Shi Cheng*, Chun Zhao, Jingjin Wu, Yuhui Shi

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

13 Citations (Scopus)

Abstract

In this paper, we utilized particle swarm optimization algorithm to solve a regression analysis problem in dielectric relaxation field. The regression function is a nonlinear, constrained, and difficult problem which is solved by traditionally mathematical regression method. The regression process is formulated as a continuous, constrained, single objective problem, and each dimension is dependent in solution space. The object of optimization is to obtain the minimum sum of absolute difference values between observed data points and calculated data points by the regression function. Experimental results show that particle swarm optimization can obtain good performance on regression analysis problems.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Pages55-63
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2013
Event4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China
Duration: 12 Jun 201215 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7928 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Advances in Swarm Intelligence, ICSI 2013
Country/TerritoryChina
CityHarbin
Period12/06/1215/06/12

Keywords

  • Particle swarm optimization
  • regression analysis
  • regression models
  • weighted least absolute difference value

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