Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of...Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.展开更多
BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some met...BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer （PSO）, guaranteed convergence particle swarm optimizer （GCPSO）, an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.展开更多
Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and dif...Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and different ratios of the bases of the trapezoidal outlet were generated based on curvature control regulation.Computational Fluid Dynamics(CFD)simulations have been conducted to obtain the flow field in the nozzle,and Forward-Backward Iterative Physical Optics(FBIPO)method was applied to study the electromagnetic scattering characteristics of the nozzle.Guarantee Convergence Particle Swarm Optimization(GCPSO)algorithm based on Radial Basis Function(RBF)neural network was used to optimize the geometry of the nozzle in consideration of its aerodynamic and electromagnetic scattering characteristics.The results show that the GCPSO method based on RBF can be used to optimize the aerodynamic characteristics of the internal flow and the scattering characteristics of the cavity of the serpentine nozzle with irregular outlet.The optimized model has a higher center offset and a lower ratio of the bases of the trapezoidal outlet after optimization compared to the original model.The optimized model leads to a slight change in aerodynamic performance,with a total pressure recovery coefficient increase of 0.31%and a discharge coefficient increase of 0.41%.In addition,the Radar Cross Section(RCS)decreases also by around 83.33%and the overall performance is significantly improved,with a decrease of the optimized objective function by around 38.74%.展开更多
基金supported by the Aeronautical Science Fund of Shaanxi Province of China(20145596025)
文摘Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.
文摘BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer （PSO）, guaranteed convergence particle swarm optimizer （GCPSO）, an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.
基金the financial support of the Fundamental Research Funds for the Central Universities(No.31020190MS708)。
文摘Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and different ratios of the bases of the trapezoidal outlet were generated based on curvature control regulation.Computational Fluid Dynamics(CFD)simulations have been conducted to obtain the flow field in the nozzle,and Forward-Backward Iterative Physical Optics(FBIPO)method was applied to study the electromagnetic scattering characteristics of the nozzle.Guarantee Convergence Particle Swarm Optimization(GCPSO)algorithm based on Radial Basis Function(RBF)neural network was used to optimize the geometry of the nozzle in consideration of its aerodynamic and electromagnetic scattering characteristics.The results show that the GCPSO method based on RBF can be used to optimize the aerodynamic characteristics of the internal flow and the scattering characteristics of the cavity of the serpentine nozzle with irregular outlet.The optimized model has a higher center offset and a lower ratio of the bases of the trapezoidal outlet after optimization compared to the original model.The optimized model leads to a slight change in aerodynamic performance,with a total pressure recovery coefficient increase of 0.31%and a discharge coefficient increase of 0.41%.In addition,the Radar Cross Section(RCS)decreases also by around 83.33%and the overall performance is significantly improved,with a decrease of the optimized objective function by around 38.74%.
