Continuous quantum Particle Swarm Optimization and its application to optimization calculation and analysis of energy-saving motor used in beam pumping unit

In this paper, a new kind of quantum particle swarm algorithm for continuous space optimization is proposed, in which quantum computation is introduced into PSO. The particles can be described as superposition of multiple states. Quantum bits are updated by quantum rotation gates, and mutated by quantum non-gates. The numerical simulation results show that the new algorithm has better stability, global search capability and faster convergence rate than classical PSO and GA. Furthermore, the new algorithm is applied to the structure optimization of 37kW energy-saving motor used in beam pumping unit successfully. The main structure parameters of motor are selected as optimal variables. And the performance of motor is chosen as constraint conditions. Motor efficiency is selected as optimization goal. The optimized energy-saving motor, which is more energy efficient and materials saving, meets the requirements of periodic pulsating variable load, low load and energy saving in oil field.