Multimodal Design Optimization of V-Shaped Magnet IPM Synchronous Machines

Vidanalage, Buddhika De Silva Guruwatta; Toulabi, Mohammad Sedigh; Filizadeh, Shaahin

doi:10.1109/tec.2018.2807618

Cited by 26 publications

(12 citation statements)

References 15 publications

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“…The experimental results show that the overall performance of DIDE is better than the other 13 state-of-theart multimodal optimization algorithms. In the future, we will extend DIDE to deal with some MMOPs in real-world applications, such as finding multiple Nash equilibria in electricity markets [1] and the multimodal design optimization of permanent magnet synchronous machine [2]. Moreover, we will test DIDE's performance in solving other kinds of problems such as multiobjective optimization problems [43]- [45].…”

Section: Discussionmentioning

confidence: 99%

“…End If 7: End For are regenerated according to (2). Note that the virtual individuals are only used to assist the mutation operation and only their position information is used in (1).…”

Section: A Virtual Population-based Mutationmentioning

confidence: 99%

“…to problems that have multiple optimal solutions, which are common in real-world applications [1], [2]. Generally speaking, each optimum in MMOPs is also regarded as a peak and the algorithms are required to locate multiple peaks and to refine the solution accuracy on the found peaks.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

Chen

Zhan

Wang

et al. 2020

IEEE Trans. Evol. Computat.

115

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Locating more peaks and refining the solution accuracy on the found peaks are two challenging issues in solving multimodal optimization problems (MMOPs). To deal with these two challenges, a distributed individuals differential evolution (DIDE) algorithm is proposed in this article based on a distributed individuals for multiple peaks (DIMP) framework and two novel mechanisms. First, the DIMP framework provides sufficient diversity by letting each individual act as a distributed unit to track a peak. Based on the DIMP framework, each individual uses a virtual population controlled by an adaptive range adjustment strategy to explore the search space sufficiently for locating a peak and then gradually approach it. Second, the two novel mechanisms named lifetime mechanism and elite learning mechanism (ELM) cooperate with the DIMP framework. The lifetime mechanism is inspired by the natural phenomenon that every organism will gradually age and has a limited lifespan. When an individual runs out of its lifetime and also has good fitness, it is regarded as an elite solution and will be added to an archive. Then the individual restarts a new lifetime, so as to bring further diversity to locate more peaks. The ELM is proposed to refine the accuracy of those elite solutions in the archive, being efficient in dealing with the solution accuracy issue on the found peaks. The experimental results on 20 multimodal benchmark test functions show that the proposed DIDE algorithm has generally better or competitive performance compared with the state-of-the-art multimodal optimization algorithms.

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Section: Discussionmentioning

confidence: 99%

“…End If 7: End For are regenerated according to (2). Note that the virtual individuals are only used to assist the mutation operation and only their position information is used in (1).…”

Section: A Virtual Population-based Mutationmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

Chen

Zhan

Wang

et al. 2020

IEEE Trans. Evol. Computat.

115

View full text Add to dashboard Cite

show abstract

“…In the field of power system, Goharrizi et al (2015) proposed a parallel MMOP algorithm to find multiple optima for the design of a voltage-sourceconverter-based high-voltage-direct-current transmission system. In electromagnetism, the design of permanent magnet synchronous machine also has multiple optimal solutions (Vidanalage et al 2018). In mathematics, some research focused on finding multiple optima of nonlinear equation systems by using EC algorithms (Song et al 2015;Gong et al 2017).…”

Section: Extending Application Fieldmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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“…The FEA-based models usually have high accuracy but are more time-consuming due to massive high-quality meshing [19]. Many research studies have been devoted to solving the computational efficiency problem of FEA-based optimisations, including the methods that combine FEA with various types of the stochastic algorithm [20,21], the computationally efficient (CE) FEA methods [21][22][23], and the design of experiment (Doe)-based methods [24,25]. However, the stochastic algorithm-based methods still use the time-consuming time-stepping (TS) FEA model as evaluating functions for multiple calculations; and the Doe methods cut the originally continuous search space into discrete points, which results in issues of local optimums.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning‐based surrogate‐assisted design optimisation of a five‐phase magnet‐shaping PMSM

Wang

Xiao

et al. 2021

IET Electric Power Appl

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Multi‐phase permanent‐magnet synchronous machines (MPMSMs) with high reliability due to sufficient fault‐tolerant capability have considerable potential for transportation electrification applications. Here, an efficient surrogate‐assisted design optimisation method is proposed based on analytical model transfer learning for torque characteristic optimisation of a five‐phase magnet‐shaping PMSM. By employing transfer learning of the source domain analytical model data and the target domain finite element analysis (FEA) data in surrogate model training, the proposed method can achieve both high accuracy and high efficiency from the merits of FEA‐ and analytical‐based optimisations, respectively. The studied machine with five‐phases and harmonic injected surface‐mounted PMs to enable harmonic injection for torque capability improvement is introduced and the analytical model is built based on the segmented PM and the complex conformal mapping methods. Besides, the optimal Latin hypercube design (LHD) and Taguchi methods are used to form the source and target domain datasets, respectively, so that data features can be efficiently captured over a wide range of optimisation variables. An optimal design is obtained by multi‐objective optimisation using the trained surrogate model, which is prototyped and measured to validate the proposed method.

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Multimodal Design Optimization of V-Shaped Magnet IPM Synchronous Machines

Cited by 26 publications

References 15 publications

Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

A survey on evolutionary computation for complex continuous optimization

Transfer learning‐based surrogate‐assisted design optimisation of a five‐phase magnet‐shaping PMSM

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