A New Kind of Optimization Method for Gear Design

2012 ◽  
Vol 190-191 ◽  
pp. 7-10 ◽  
Author(s):  
Yu Guo Wu
Keyword(s):  
Simulated Annealing Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Bp Algorithm ◽  
Mean Square ◽  
Test Results ◽  
Swarm Optimization ◽  
Design Efficiency ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, based on PSO algorithm and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, which based on PSO algorithm, and the implementing framework of PSO and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

Particle Swarm Optimization for Cylinder Helical Gear Multi-Objective Design Problems

2011 ◽  
Vol 109 ◽  
pp. 216-221
Author(s):  
Yuan Bin Mo
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Weighted Average ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Helical Gear ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Gear Design

The models for many problems in cylinder helical gear design are all multi-objective optimization. But there isn’t an effective algorithm for solving multi-objective optimization, and now the only method for it is changing multi-objective into one objective by weighted average. In this paper a novel multi-objective optimization method based on Particle Swarm Optimization (PSO) algorithm is designed for applying to solve this kind of problem. A paradigm depicted in the paper shows the algorithm is practical.

Parameter Design of Conformal PML Based on 2D Monostatic RCS Optimization

2021 ◽  
Vol 36 (6) ◽  
pp. 726-733
Author(s):  
Yongjie Zhang ◽  
Xiaofeng Deng
Keyword(s):  
Electromagnetic Scattering ◽  
Simulated Annealing Algorithm ◽  
Design Method ◽  
Optimization Method ◽  
Parameter Design ◽  
Design Parameters ◽  
Absorption Effect ◽  
Absorbing Boundary ◽  
Design And Optimization ◽  
Annealing Algorithm

In this study, 2D finite element (FE) solving process with the conformal perfectly matched layer (PML) is elucidated to perform the electromagnetic scattering computation. With the 2D monostatic RCS as the optimization objective, a sensitivity analysis of the basic design parameters of conformal PML (e.g., layer thickness, loss factor, extension order and layer number) is conducted to identify the major parameters of conformal PML that exerts more significant influence on 2D RCS. Lastly, the major design parameters of conformal PML are optimized by the simulated annealing algorithm (SA). As revealed from the numerical examples, the parameter design and optimization method of conformal PML based on SA is capable of enhancing the absorption effect exerted by the conformal PML and decreasing the error of the RCS calculation. It is anticipated that the parameter design method of conformal PML based on RCS optimization can be applied to the cognate absorbing boundary and 3D electromagnetic computation.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

Free Parameter Optimization of DTMDs Based on Improved Hybrid Genetic-Simulated Annealing Algorithm

2020 ◽  
Vol 20 (03) ◽  
pp. 2050031
Author(s):  
Qiang Han ◽  
Xuan Zhang ◽  
Kun Xu ◽  
Xiuli Du
Keyword(s):  
Simulated Annealing ◽  
Parameter Optimization ◽  
Free Parameter ◽  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Design Parameters ◽  
Mass Ratio ◽  
Tuned Mass Dampers ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

The optimum design of distributed tuned mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control performance, a free parameter optimization method (FPOM) is proposed. This method only restricts the total mass of the DTMDs system and takes the installation position, mass ratio, stiffness and damping of each TMD as parameters to be optimized. An improved hybrid genetic-simulated annealing algorithm (IHGSA) is adopted to find the optimum values of the design parameters. This algorithm can solve the non-convexity and multimodality problems of the objective function and is quite effective in dealing with the large amount of computations in the free parameter optimization. A numerical benchmark model is adopted to compare the control efficiency of FPOM with conventional control scenarios, such as single TMD, multiple TMDs and DTMDs optimized through conventional methods. The results show that the DTMDs system optimized by using FPOM is superior to the other control scenarios for the same value of mass ratio.

scholarly journals A Concept of the Non-Stationary Filtering Network with Reduced Transient Response

2019 ◽  
Vol 9 (21) ◽  
pp. 4570
Author(s):  
Katarzyna Wiechetek ◽  
Jacek Piskorowski
Keyword(s):  
Transient Response ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Gain Factor ◽  
Time Varying ◽  
Swarm Optimization ◽  
First Order ◽  
Analog System ◽  
Time Varying Parameters ◽  
Discretization Process

This paper presents a concept of the non-stationary filtering network with reduced transient response consisting of the first-order digital elements with time-varying parameters. The digital filter section is based on the analog system. In order to design the filtering network, the analog prototype was subjected to the discretization process. The time constant and the gain factor were then temporarily varied in time in order to suppress the transient response of the designed filtering structure. The optimization method, based on the Particle Swarm Optimization (PSO) algorithm which is aimed at reducing the settling time by a proper parameter manipulation, is presented. Simulation results proving the usefulness of the proposed concept are also shown and discussed.

scholarly journals Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Correlation Algorithm ◽  
Annealing Algorithm

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

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