Application of constrained multi-objective hybrid quantum particle swarm optimization for improving performance of an ironless permanent magnet linear motor

Wen Jong Chen, Wen Cheng Su, Yin Liang Yang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This study presents an ironless permanent magnet linear brushless motor (PMLBM) with three objective functions: maximal thrust force, minimal temperature, and minimal volume. Using response surface methodology (RSM), this study presents a mathematical predictive model with constraints using the penalty functions concept for each objective function. The design variables in this study include magnetic width, magnetic height, magnetic pitch, air-gap, coil width, coil height, and coil diameter. This study uses an elitist hybrid quantum behavior particle swarm optimization algorithm with mutation to solve this multi-objective optimization problem (EMOHQPSO). This elitist mechanism with crowding distance sorting improves the number and diversity of the solutions. Results show that the proposed approach is superior to the non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO), respectively, on the 3D graph Pareto-optimal front. Compared to the initial motor, the thrust force increased by 6.27%, the thrust density increased by 14.9%, and the temperature and volume decreased by 14.03% and 6.25% respectively. These results confirm the satisfactory performance of the proposed solutions.

Original languageEnglish
Pages (from-to)3111-3120
Number of pages10
JournalApplied Mathematics and Information Sciences
Volume8
Issue number6
DOIs
Publication statusPublished - 2014

Fingerprint

Linear Motor
Linear motors
Permanent Magnet
Coil
Particle swarm optimization (PSO)
Permanent magnets
Particle Swarm Optimization
Sorting
Objective function
Response Surface Methodology
NSGA-II
Sorting algorithm
Predictive Model
Penalty Function
Multiobjective Optimization Problems
Multiobjective optimization
Particle Swarm Optimization Algorithm
Multi-objective Optimization
Mutation
Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Analysis
  • Numerical Analysis
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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title = "Application of constrained multi-objective hybrid quantum particle swarm optimization for improving performance of an ironless permanent magnet linear motor",
abstract = "This study presents an ironless permanent magnet linear brushless motor (PMLBM) with three objective functions: maximal thrust force, minimal temperature, and minimal volume. Using response surface methodology (RSM), this study presents a mathematical predictive model with constraints using the penalty functions concept for each objective function. The design variables in this study include magnetic width, magnetic height, magnetic pitch, air-gap, coil width, coil height, and coil diameter. This study uses an elitist hybrid quantum behavior particle swarm optimization algorithm with mutation to solve this multi-objective optimization problem (EMOHQPSO). This elitist mechanism with crowding distance sorting improves the number and diversity of the solutions. Results show that the proposed approach is superior to the non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO), respectively, on the 3D graph Pareto-optimal front. Compared to the initial motor, the thrust force increased by 6.27{\%}, the thrust density increased by 14.9{\%}, and the temperature and volume decreased by 14.03{\%} and 6.25{\%} respectively. These results confirm the satisfactory performance of the proposed solutions.",
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