Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

2018 ◽  
Vol 37 (2) ◽  
pp. 549-564 ◽  
Author(s):  
Bourahla Kheireddine ◽  
Belli Zoubida ◽  
Hacib Tarik ◽  
Achoui Imed
Keyword(s):  
Optimal Design ◽  
Optimization Problems ◽  
Computation Time ◽  
Electrical Machines ◽  
Neuron Network ◽  
Element Analysis ◽  
Content Type ◽  
Switched Reluctance ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

PurposeThis study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.Design/methodology/approachThe optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.FindingsTo improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.Originality/valueThe proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.

scholarly journals Optimal Design of Passive and Active Control Systems in Seismic-excited Structures Using a New Modified TLBO

2020 ◽  
Author(s):  
Saeed Hosseinaei ◽  
Mohammad Reza Ghasemi ◽  
Sadegh Etedali
Keyword(s):  
Optimal Design ◽  
Structural Control ◽  
Passive Control ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Control Device ◽  
Feedback Gain ◽  
Active Structural Control ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Vibration control devices have recently been used in structures subjected to wind and earthquake excitations. The optimal design problems of the passive control device and the feedback gain matrix of the controller for the seismic-excited structures are some attractive problems for researches to develop optimization algorithms with the advancement in terms of simplicity, accuracy, speed, and efficacy. In this paper, a new modified teaching–learning-based optimization (TLBO) algorithm, known as MTLBO, is proposed for the problems. For some benchmark optimization functions and constrained engineering problems, the validity, efficacy, and reliability of the MTLBO are firstly assessed and compared to other optimization algorithms in the literature. The undertaken statistical indicate that the MTLBO performs better and reliable than some other algorithms studied here. The performance of the MTLBO will then be explored for two passive and active structural control problems. It is concluded that the MTLBO algorithm is capable of giving better results than conventional TLBO. Hence, its utilization as a simple, fast, and powerful optimization tool to solve particular engineering optimization problems is recommended.

scholarly journals An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Feng Zou ◽  
Debao Chen ◽  
Jiangtao Wang
Keyword(s):  
Optimization Problems ◽  
Global Solutions ◽  
Function Optimization ◽  
Improved Method ◽  
Social Character ◽  
The Social ◽  
Teaching Learning Based Optimization ◽  
The Mean ◽  
The Individual ◽  
Teaching Learning

An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Analysis of torque ripple and fault-tolerant capability for a 16/10 segmented switched reluctance motor in HEV applications

2019 ◽  
Vol 38 (6) ◽  
pp. 1725-1737
Author(s):  
Xiaodong Sun ◽  
Jiangling Wu ◽  
Shaohua Wang ◽  
Kaikai Diao ◽  
Zebin Yang
Keyword(s):  
Fault Tolerant ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Experimental Results ◽  
Element Analysis ◽  
Content Type ◽  
Switched Reluctance ◽  
Output Torque ◽  
Reluctance Motor ◽  
Lower Torque

Purpose The torque ripple and fault-tolerant capability are the two main problems for the switched reluctance motors (SRMs) in applications. The purpose of this paper, therefore, is to propose a novel 16/10 segmented SRM (SSRM) to reduce the torque ripple and improve the fault-tolerant capability in this work. Design/methodology/approach The stator of the proposed SSRM is composed of exciting and auxiliary stator poles, while the rotor consists of a series of discrete segments. The fault-tolerant and torque ripple characteristics of the proposed SSRM are studied by the finite element analysis (FEA) method. Meanwhile, the characteristics of the SSRM are compared with those of a conventional SRM with 8/6 stator/rotor poles. Finally, FEA and experimental results are provided to validate the static and dynamic characteristics of the proposed SSRM. Findings It is found that the proposed novel 16/10 SSRM for the application in the belt-driven starter generator (BSG) possesses these functions: less mutual inductance and high fault-tolerant capability. It is also found that the proposed SSRM provides lower torque ripple and higher output torque. Finally, the experimental results validate that the proposed SSRM runs with lower torque ripple, better output torque and fault-tolerant characteristics, making it an ideal candidate for the BSG and similar systems. Originality/value This paper presents the analysis of torque ripple and fault-tolerant capability for a 16/10 segmented switched reluctance motor in hybrid electric vehicles. Using FEA simulation and building a test bench to verify the proposed SSRM’s superiority in both torque ripple and fault-tolerant capability.

Synthesis of thinned planar antenna arrays using teaching–learning-based optimization

2014 ◽  
Vol 7 (5) ◽  
pp. 557-563 ◽  
Author(s):  
Nihad I. Dib
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Optimization Method ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Planar Antenna ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Level Reduction ◽  
Better Than

In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching–learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained using other evolutionary algorithms.

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