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