Application of Constrained Multi-Objective Hybrid Quantum Particle Swarm optimization for Improving Performance of an Ironless Permanent Magnet Linear Motor

作者：Chen Wen Jong^*; Su Wen Cheng; Yang Yin Liang

来源：Applied Mathematics & Information Sciences, 2014, 8(6): 3111-3120.

DOI：10.12785/amis/080652

• QPSO
• RSM
• Pareto-optimal front
• Linear motor

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.

• 出版日期2014-11