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Application of Constrained Multi-Objective Hybrid Quantum Particle Swarm optimization for Improving Performance of an Ironless Permanent Magnet Linear Motor |
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PP: 3111-3120 |
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Author(s) |
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Wen-Jong Chen,
Wen-Cheng Su,
Yin-Liang Yang,
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Abstract |
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| 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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