scholarly journals Optimization for the Redundancy Allocation Problem of Reliability Using an Improved Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
H. Marouani
Keyword(s):  
Particle Swarm Optimization ◽  
System Reliability ◽  
Particle Swarm ◽  
Classical Particle ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Redundancy Allocation ◽  
Improved Particle Swarm Optimization ◽  
Efficiency And Effectiveness ◽  
In Series

This paper presents an enhanced and improved particle swarm optimization (PSO) approach to overcome reliability-redundancy allocation problems in series, series-parallel, and complex systems. The problems mentioned above can be solved by increasing the overall system reliability and minimizing the system cost, weight, and volume. To achieve this with these nonlinear constraints, an approach is developed based on PSO. In particular, the inertia and acceleration coefficients of the classical particle swarm algorithm are improved by considering a normal distribution for the coefficients. The new expressions can enhance the global search ability in the initial stage, restrain premature convergence, and enable the algorithm to focus on the local fine search in the later stage, and this can enhance the perfection of the optimization process. Illustrative examples are provided as proof of the efficiency and effectiveness of the proposed approach. Results show that the overall system reliability is far better when compared with that of some approaches developed in previous studies for all three tested cases.

Solving Nonlinear Optimization Problems of Steering Trapezoid Mechanism Based on an IPSO

2014 ◽  
Vol 945-949 ◽  
pp. 607-613
Author(s):  
Ling Liu ◽  
Pei Zhou ◽  
Jun Luo ◽  
Zan Pi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Steering Wheel ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Rotational Angle ◽  
Improved Particle Swarm Optimization

The paper focus on an improved particle swarm optimization (IPSO) used to solve nonlinear optimization problems of steering trapezoid mechanism. First of all, nonlinear optimization model of steering trapezoid mechanism is established. Sum of absolute value of difference between actual rotational angle of anterolateral steering wheel and theoretical rotational angle of anterolateral steering wheel is taken as objective function, bottom angle and steering arm length of steering trapezoid mechanism are selected to be design variables. After that, an improved particle swarm optimization algorithm is proposed by introducing Over-flow exception dealing functions to deal with complicated nonlinear constraints. Finally, codes for IPSO are programmed and parameters of steering trapezoid mechanism for different models are optimized, and numerical result shows that errors of objective function's ideal values and objective function's optimization values are minimal. Performance comparison experiment of different intelligent algorithms indicates that the proposed new algorithm is superior to Particle swarm algorithm based on simulated annealing (SA-PSO) and traditional particle swarm optimization (TPSO) in good and fast convergence and small calculating quantity, but a little inferior to particle swarm algorithm based on simulated annealing (SA-PSO) in calculation accuracy in the process of optimization.

scholarly journals Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaoyan Ding ◽  
Yingying Cao ◽  
Fengtao Sun ◽  
Airong Ma ◽  
Feiyue Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Particle Swarm Optimization ◽  
Magnetic Resonance ◽  
Placenta Accreta ◽  
Particle Swarm ◽  
Clinical Analysis ◽  
Particle Swarm Algorithm ◽  
Resonance Imaging ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The magnetic resonance imaging (MRI) image processing capabilities were investigated based on the improved particle swarm optimization (IPSO) algorithm, and the clinical application analysis of MRI images in the diagnosis of placenta accreta (PA) was evaluated in this study. The MRI uterine images were detected on the basis of IPSO. Besides, the clinical data of 89 patients with PA were selected and collected, who were diagnosed by clinical cesarean section surgery and pathological comprehensive diagnosis in hospital from January 2018 to July 2020. Then, all of them underwent the ultrasound (US) and MRI examinations, and the differences of sensitivity, specificity, and accuracy between MRI and US under IPSO in the diagnosis of PA were compared, as well as the differences in the diagnosis of adhesive, implantable, and penetrated PA. The results showed that the difference in detection between IPSO-based MRI images and US images was not statistically substantial ( p > 0.05 ), but the number of initial detections was higher than the number of US examination. MRI examination had higher sensitivity and specificity in the diagnosis of PA during pregnancy, especially for implantable PA, compared with US examination ( p < 0.05 ). In conclusion, MRI images based on the improved particle swarm optimization algorithm showed a good application effect in the diagnosis of placental implantation diseases, which was worthy of further promotion in clinical practice.

scholarly journals Particle Swarm Optimization Algorithm in Improved Electrical Control System

2021 ◽  
Vol 2066 (1) ◽  
pp. 012020
Author(s):  
Xiaotao Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Control Systems ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Electrical Control

Abstract In today’s social background where high-tech emerges endlessly, various production fields in our country have fully entered the era of mechanical automation and electrical automation, and electrical control systems have been widely used in our country’s electrical appliance manufacturing industry. This paper is based on the theoretical analysis of the particle swarm optimization algorithm. Based on this optimization algorithm, a brand-new particle swarm optimization algorithm is obtained. It is applied to the electrical control system to improve it and makes full use of the improved particle swarm optimization algorithm. The existing electrical control system is optimized. This article firstly analyzes the types of common electrical control systems, puts forward some basic methods to improve the control system, and then explains the effective techniques for improvement, hoping to make reference to the improvement of electrical control systems later in this article. This article first improves the particle swarm optimization algorithm, adding the ability to adjust the control system and dynamic learning factors, focusing on strengthening the later stage of the optimization of the particle swarm algorithm and the ability to converge to improve the efficiency of the calculation. The second is to improve the traditional particle swarm optimization algorithm and update the calculation method of the formula to reduce the possibility of selecting undesirable particles and affecting the optimization results. Finally, through MATLAB and reverse simulation analysis, compared with the traditional electrical control system algorithm, the improved particle swarm optimization algorithm has a faster convergence speed and high control system efficiency. The experimental research results show that the particle swarm optimization algorithm proposed in this paper has a huge advantage compared with other algorithms, and its parameter optimization gives full play to the powerful performance of the electrical control system.

scholarly journals Study of the Magnetic Properties of Haematite Based on Spectroscopy and the IPSO-ELM Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yachun Mao ◽  
Chongmin Liu ◽  
Dong Xiao ◽  
Jichun Wang ◽  
Ba Tuan Le
Keyword(s):  
Magnetic Properties ◽  
Particle Swarm Optimization ◽  
Prediction Models ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Good Prediction ◽  
Swarm Optimization ◽  
Generalization Ability ◽  
Improved Particle Swarm Optimization ◽  
Hidden Layer

The detection of the magnetic properties of haematite plays an important role in the adjustment of the beneficiation process of haematite and the improvement of metal recovery. The existing methods for measuring the magnetic properties of iron ore either have large errors or take a long time. Therefore, it is very necessary to find a method that can quickly and accurately detect the magnetic properties of haematite. This paper presents a method to detect the magnetic properties of haematite based on the extreme learning machine based on the improved particle swarm optimization (IPSO-ELM) algorithm and spectroscopy. The improved particle swarm optimization algorithm is used to optimize the input weights, hidden layer deviations, and hidden layer nodes of the ELM network. Introducing the linear decreasing inertia weight for the particle swarm algorithm, taking into account the norm of the output weight in the particle update process and using the variation idea to change the length of the particle give the IPSO-ELM better stability and generalization ability. The experimental results show that the IPSO-ELM prediction model has a good prediction performance and has better generalization ability than that of the ELM and PSO-ELM prediction models. Compared with traditional chemical analysis methods and manual methods, this method has great advantages in terms of economy, speed, and accuracy.

Optimization Technology of PID Parameter in Control System Based on Improved Particle Swarm Optimization Algorithms

2014 ◽  
Vol 908 ◽  
pp. 547-550
Author(s):  
Tian Shun Huang ◽  
Xiao Qiang Li ◽  
Hong Yun Lian ◽  
Zhi Qiang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithm

Particle swarm algorithm has been proven to be very good solving many global optimization problems. Firstly we improved particle swarm optimization algorithm, the improved PSO algorithm for continuous optimization problem, in solving the nonlinear combinatorial optimization problems and mixed integer nonlinear optimization problems is very effective. This design adopts the improved particle swarm algorithm to optimize the PID parameters of the control system, and the effectiveness of the improved algorithm is proved by experiment.

A comprehensively improved particle swarm optimization algotithm to guarantee particle activity

2021 ◽  
pp. 94-101
Author(s):  
Ya Bi ◽  
◽  
Anthony Lam ◽  
Huiqun Quan ◽  
Hui Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Local Extremum ◽  
Particle Swarm Algorithm ◽  
Improvement Strategy ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Optimization Search

The particle swarm optimization algorithm has the disadvantages, for instance, the convergence viscosity of the algorithm is reduced at the post evolution phase, the optimization search efficiency is reduced, the algorithm is easy to be inserted with local extremum during the calculation of complex problem of high-dimensional multiple extremum, and the convergence thereof is low. As to the disadvantage of the PSO, we proposed a particle swarm optimization of comprehensive improvement strategy, which is a simple particle swarm optimization with dynamic adaptive hybridization of extremum disturbance and cross (ecds-PSO algorithm). This new comprehensive improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. The hybridization operation of increasing the extremum disturbance and introducing genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and a plurality of comparative experiment provide us the following information: the improved particle swarm optimization is a simple and effective optimization algorithm which can improve the algorithm accuracy, convergence viscosity and ability of avoiding the local extremum, and effectively reduce the calculation complexity.

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