To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,an...To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.展开更多
This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and th...This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit,BGSO is applied to optimize the on/off state of the unit,and the Lambda-iteration method is adopted to solve the economic dispatch problem.In the iterative process,the solutions that do not satisfy all the constraints are adjusted by the correction method.Furthermore,different adjustment techniques such as conversion from cold start to hot start,decommitment of redundant unit,are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions.The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantuminspired evolutionary algorithm(QEA),improved binary particle swarm optimization(IBPSO)and mixed integer programming(MIP).Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.展开更多
基金This research was funded by the Hebei Science and Technology Support Program Project(19273703D)the Hebei Higher Education Science and Technology Research Project(ZD2020318).
文摘To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.
文摘This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit,BGSO is applied to optimize the on/off state of the unit,and the Lambda-iteration method is adopted to solve the economic dispatch problem.In the iterative process,the solutions that do not satisfy all the constraints are adjusted by the correction method.Furthermore,different adjustment techniques such as conversion from cold start to hot start,decommitment of redundant unit,are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions.The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantuminspired evolutionary algorithm(QEA),improved binary particle swarm optimization(IBPSO)and mixed integer programming(MIP).Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.
