scholarly journals Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2000
Author(s):  
Jin-Hwan Lee ◽  
Woo-Jung Kim ◽  
Sang-Yong Jung
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Cost Function ◽  
Particle Swarm ◽  
Torque Ripple ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Traction Motor ◽  
Target Performance ◽  
The Cost

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.

The Influence of Inertia Weight on Particle Swarm Optimization in Boundary Element Inverse Analysis for Rebar Corrosion Detection

2013 ◽  
Vol 686 ◽  
pp. 266-272 ◽  
Author(s):  
Syarizal Fonna ◽  
M. Ridha ◽  
Syifaul Huzni ◽  
Ahmad Kamal Ariffin
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Function ◽  
Boundary Element ◽  
Inverse Analysis ◽  
Particle Swarm ◽  
Concrete Surface ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Rebar Corrosion ◽  
The Cost

Particle Swarm Optimization (PSO) has been applied as optimization tool in various engineering problems. Inverse analysis is one of the potential application fields for PSO. In this research, the behavior of PSO, related to its inertia weight, in boundary element inverse analysis for detecting corrosion of rebar in concrete is studied. Boundary element inverse analysis was developed by combining BEM and PSO. The inverse analysis is carried out by means of minimizing a cost function. The cost function is a residual between the calculated and measured potentials on the concrete surface. The calculated potentials are obtained by solving the Laplace’s equation using BEM. PSO is used to minimize the cost function. Thus, the corrosion profile of concrete steel, such as location and size, can be detected. Variation in its inertia weight was applied to analyze the behavior of PSO for inverse analysis. The numerical simulation results show that PSO can be used for the inverse analysis for detecting rebar corrosion by combining with BEM. Also, it shows different behavior in minimizing cost function depending on inertia weight.

scholarly journals An Image Steganography using Particle Swarm Optimization and Transform Domain

2018 ◽  
Vol 7 (2.24) ◽  
pp. 474 ◽  
Author(s):  
Sanjutha MK
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Function ◽  
Fitness Function ◽  
Particle Swarm ◽  
Image Steganography ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Secret Image ◽  
The Cost

As information technology is growing tremendously, one of the major concern is information security. A technique called image steganography is used to provide better security and for safeguarding the information. In image steganography, a secret image is put into recipient image so that only the receiver and sender will be aware of the secret message. Here in this paper, a secure, optimized scheme called particle swarm optimization is used to select the pixel efficiently for embedding the secret image in to cover image. PSO(Particle Swarm Optimization) decides pixel using fitness function which is based on the cost function. Cost function calculates entropy, edge and pixels intensity to evaluate fitness. Also, a technique called discrete wavelet transform has been employed to achieve robustness and statistical undetectability. The main aim of the proposed paper is to make better security and to obtain efficient PSNR and MSE values  

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

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