Feature Selection for Mahalanobis-Taguchi System with Chaotic Quantum Behavior Particle Swarm Optimization

Xiao-hong PENG, Rui ZHENG, Jiu-fu LIU

Abstract


This paper proposes a Mahalanobis-Taguchi system variable optimization method based on chaotic quantum behavior particle swarm, and solve the MTS multi-objective optimization problem by using the chaotic quantum behavior particle swarm optimization algorithm to reduce the feature variables and improve the computational efficiency and accuracy of optimization.

Keywords


Mahalanobis–Taguchi system, V ariable optimization method, Chaotic quantum behavior particle swarm, Optimization


DOI
10.12783/dtcse/cscme2019/32535

Refbacks

  • There are currently no refbacks.