Simulated annealing and genetic algorithms in topology optimization tools: A comparison through the design of a Switched Reluctance Machine

Denies, Jonathan; Dehez, Bruno; Hamid, Ben Ahmed

doi:10.1109/speedam.2010.5545100

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…The results show that the SA has initially reached convergence after evolution of 10%, but the GA still does not reach convergence after evolution of 40%, as shown in Figure 18. The results prove that the SA has higher convergence in the multiobjective optimization of the reluctance machine [70]. According to the actual design requirements, the simulation model of the electric vehicle is established in MATLAB and the geometric parameters related to SRM are optimized by the Taguchi-chicken swarm optimization algorithm (Taguchi-CSO) [72].…”

Section: Figure 17mentioning

confidence: 97%

Overview of the Optimal Design of the Electrically Excited Doubly Salient Variable Reluctance Machine

Zhao

et al. 2021

Energies

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The Electrically Excited Doubly Salient Variable Reluctance Machine (EEDSVRM) is a new type of brushless machine designed according to the principle of air gap reluctance change. There is neither permanent magnet steel nor excitation winding on the rotor. The rotor is made of silicon steel sheets, thus the structure of the variable reluctance machine is very simple. There are many optimization methods for this type of machine optimal design, such as novel machine topology optimization, finite element simulation-based optimization, mathematical analysis-based optimization, intelligent algorithm-based optimization, and multiple fusion-based optimization. Firstly, this article introduces the basic structure and working principle of the EEDSVRM and analyzes both its common regularity and individual difference. Then, the different optimization design methods of EEDSVRM are reviewed, the advantages and disadvantages of the different optimization methods are summarized, and the research interests of the optimization design of variable reluctance machines in the future are prospected.

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Section: Figure 17mentioning

confidence: 97%

Overview of the Optimal Design of the Electrically Excited Doubly Salient Variable Reluctance Machine

Zhao

et al. 2021

Energies

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“…In literature, arc angles of rotor and stator poles, the height of rotor and stator poles, stator/rotor back iron thickness, number of phases, and stack length were used to minimize the machine torque ripple. Also, the material distribution inside the rotor and stator cores was optimized to reduce the torque ripple of SRMs [63]- [64].…”

Section: A Torque Ripplesmentioning

confidence: 99%

“…Simulated annealing algorithm is one of the stochastic optimization methods used extensively in the literature in electrical machines design optimization [130], [64], [115]. The algorithm is based on the heat treatment process of steel.…”

Section: B) Simulated Annealing Optimizationmentioning

confidence: 99%

“…Stochastic optimization techniques such GA [136], [115] , [150]- [152], PSO [153]- [154], and SA [115], [64], were used for multi-objective optimization of SRMs in the literature. A more detailed literature review of the singleobjective and the multi-objective optimization of SRMs is discussed in sections V and VI.…”

Section: Simulation Numbermentioning

confidence: 99%

“…The same geometric parameters were optimized in [64] to improve the power density of a two-phase 4/2 unidirectional radial-flux SRM by GA and SA. The SA algorithm was found to be more effective as it increased the machine power density by ~27 % as compared to an 11.7 % increase using GA.…”

Section: Geometry Optimization Of Srmsmentioning

confidence: 99%

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Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Abdalmagid

Sayed²,

Bakr

et al. 2022

IEEE Access

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Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine.

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Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict

Marwala

Lagazio

2011

Advanced Information and Knowledge Processing

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Simulated annealing and genetic algorithms in topology optimization tools: A comparison through the design of a Switched Reluctance Machine

Cited by 10 publications

References 8 publications

Overview of the Optimal Design of the Electrically Excited Doubly Salient Variable Reluctance Machine

Overview of the Optimal Design of the Electrically Excited Doubly Salient Variable Reluctance Machine

Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict

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