Evolution strategies and multi-objective optimization of permanent magnet motor

Andersen, Søren Bøgh; Santos, Ilmar

doi:10.1016/j.asoc.2011.10.013

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…Most of the metaheuristic techniques can be used to solve global optimization problems with nonlinear constraint by using metaheuristic algorithms; there is a high possibility to determine a near optimal solution, which can be considered by designer and engineering as a global optimum [22].…”

Section: Optimization and Control Of Electrical Machinesmentioning

confidence: 99%

“…One of the most promising algorithms from the class of evolutionary algorithms widely used in the field of electric machines is Differential Evolution (DE) [22][23][24] first introduced by Price and Storn [25] in 1995. The algorithm was later improved and named Generalized Differential Evolution (GDE) (extended DE for constrained multi-objective optimization) by Lampinen…”

Section: Optimization and Control Of Electrical Machinesmentioning

confidence: 99%

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Introductory Chapter: Introduction to the Design and the Control of Electrical Machines

Aissaoui¹

2018

Optimization and Control of Electrical Machines

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Section: Optimization and Control Of Electrical Machinesmentioning

confidence: 99%

Section: Optimization and Control Of Electrical Machinesmentioning

confidence: 99%

Introductory Chapter: Introduction to the Design and the Control of Electrical Machines

Aissaoui¹

2018

Optimization and Control of Electrical Machines

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“…ГА не требуют заданной отправной точки, позволяет использовать нелинейные, дискретные целевые функции и условия ограничения. Хотя он не строго математически гарантирует, что оптимальные решения будут найдены, но существует высокая вероятность того, что близкое к оптимальному решение будет найдено [7]. Множество этих решений находится очень близко к настоящему Парето-фронту и называется аппроксимацией множества Парето.…”

Section: постановка задачи оптимизации параметров и выбор метода оптиunclassified

Genetic Algorithm in Optimization Design of Interior Permanent Magnet Synchronous Motor

Ле¹

2017

Sist. anal.i prikl. inform.

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Classical method of designing electric motors help to achieve functional motor, but doesn’t ensure minimal cost in manufacturing and operating. Recently optimization is becoming an important part in modern electric motor design process. The objective of the optimization process is usually to minimize cost, energy loss, mass, or maximize torque and efficiency. Most of the requirements for electrical machine design are in contradiction to each other (reduction in volume or mass, improvement in efficiency etc.). Optimization in design permanent magnet synchronous motor (PMSM) is a multi-objective optimization problem. There are two approaches for solving this problem, one of them is evolution algorithms, which gain a lot of attentions recently. For designing PMSM, evolution algorithms are more attractive approach. Genetic algorithm is one of the most common. This paper presents components and procedures of genetic algorithms, and its implementation on computer. In optimization process, analytical and finite element method are used together for better performance and precision. Result from optimization process is a set of solutions, from which engineer will choose one. This method was used to design a permanent magnet synchronous motor based on an asynchronous motor type АИР112МВ8.

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“…With restrictions that vary and some objective functions in solving optimization problems become the distinct advantage for evolutionary algorithms (Datta and Regis, 2016, Yoosefelahi et al, 2012, Andersen and Santos, 2012. As one of the evolutionary algorithms that implement processes of recombination and mutation, evolution strategies have proven to provide quality solutions to the optimization problem refers to several studies that already exist (Munawaroh and Mahmudy, 2015, Milah and Mahmudy, 2015, Vista and Mahmudy, 2015.…”

Section: Introductionmentioning

confidence: 99%

Profit Optimization Based on Total Production in Textile Home Industry Using Evolution Strategies Algorithms

Khuluqi

Mahmudy

Rahmi

2016

IJLEG

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Profit optimization became one of the main goals of the production process of home industry. A maximum profit can be achieved with proper planning of production. In the process of implementation, production planning has many constraints such as lot sizing, limited stock, overtime work and the many products derived from the same source. To address the problem, we develop computer software using a heuristic method called evolution strategy (ES). ES has capability to solve optimization problems with nearly optimal results. The result of the calculation process of evolution strategy algorithm was compared with data from the source. Computational analysis shows that ES produces a production plan that has profit of Rp 5,324,000. It is bigger than manual production plan that has profit of Rp 2,747,000.

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Evolution strategies and multi-objective optimization of permanent magnet motor

Cited by 15 publications

References 13 publications

Introductory Chapter: Introduction to the Design and the Control of Electrical Machines

Introductory Chapter: Introduction to the Design and the Control of Electrical Machines

Genetic Algorithm in Optimization Design of Interior Permanent Magnet Synchronous Motor

Profit Optimization Based on Total Production in Textile Home Industry Using Evolution Strategies Algorithms

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