Particle Swarm Optimization in Regression Analysis: A Case Study

  • Shi Cheng
  • Chun Zhao
  • Jingjin Wu
  • Yuhui Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)


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.


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


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shi Cheng
    • 1
    • 2
  • Chun Zhao
    • 1
    • 2
  • Jingjin Wu
    • 1
    • 2
  • Yuhui Shi
    • 2
  1. 1.Department of Electrical Engineering and ElectronicsUniversity of LiverpoolLiverpoolUK
  2. 2.Department of Electrical & Electronic EngineeringXi’an Jiaotong-Liverpool UniversitySuzhouChina

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