A combined strategy for optimization in nonlinear magnetic problems using simulated annealing and search techniques

Drago, Gian Paolo; Manella, A.; Nervi, M.; Repetto, Maurizio; Secondo, G

doi:10.1109/20.123991

Cited by 51 publications

(19 citation statements)

References 4 publications

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“…A major challenge in designing a short magnet is the SA has been successfully applied to other electromagnetic design problems (e.g., [13][14][15]. By imposing length conretention of high homogeneity conditions over the diameter sensitive volume (dsv), as magnet homogeneity is strongly straints, the SA routine effectively attempts to find the best solution possible within these limits.…”

mentioning

confidence: 99%

Compact MRI Magnet Design by Stochastic Optimization

Crozier

Doddrell

1997

Journal of Magnetic Resonance

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confidence: 99%

Compact MRI Magnet Design by Stochastic Optimization

Crozier

Doddrell

1997

Journal of Magnetic Resonance

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“…Rule 1: if x is A 1 and y is B 1 then f 1 = p 1 *x+q 1 *y+r 1 Rule 2: if x is A 2 and y is B 2 then f 2 = p 2 *x+q 2 *y+r 2 Where x and y the inputs variables to the node I, A i and B i are fuzzy sets (or the linguistic table), which are characterized by convenient membership functions and finally, p i , q i and r i are the consequence parameters [6-7-10]. The structure of this inference system is shown in Fig.5.…”

Section: A Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…The optimization process by simu lated annealing was first described by Kirkpatrick et al [12], and is based on work by Metropolis et al [13] based on the Bolt zman's probability [12]. The acceptance probability of solution point i is defined by [16]:…”

Section: B Adaptive Simulated Annealingmentioning

confidence: 99%

“…Today, the change in its configurations with lo w prices has been stimu lating the design/optimizat ion for industrial applications [1][2]. In these devices, the main task is increasing of the magnetic force in electro magnetic actuator by geometrical shape optimization.…”

Section: Introductionmentioning

confidence: 99%

“…In these devices, the main task is increasing of the magnetic force in electro magnetic actuator by geometrical shape optimization. Various optimizat ion algorithms for shape optimization of an actuator have been proposed [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices

Mohdeb¹,

Hacib²

2014

IJISA

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Abstract-This paper presents a new model based on simulated annealing algorithm (ASA) and adaptive neuro-fuzzy inference system (ANFIS) for shape optimization and its applications to electromagnetic devices. The proposed model uses ANFIS system to evaluate the electromagnetic performance of the device. Both the ANFIS and ASA method are applied to the design/optimization of the electromagnetic actuator. The results of the proposed approach are compared with other techniques such as: method of moving asymptotes, penalty method, augmented lagrangian genetic algorithm and simulated annealing method (SA). Among the algorithms, the proposed ANFIS-ASA approach significantly outperforms the other methods.

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An efficient hybrid algorithm for the optimization of problems with several local minima

Alotto

Nervi

2001

Int. J. Numer. Meth. Engng.

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The problem of design optimization is of high industrial interest, and has been extensively studied for years, with excellent results. However, there is the well‐known issue of a reasonable balance between the computational effort usually required by stochastic methods, and the fact that deterministic optimizers, even though much more efficient, are not guaranteed to localize a good minimum, as they can remain trapped in the first found local one. To overcome these problems, the authors developed a hybrid strategy, which gave good results in terms of speed and reliability of the obtained optima, especially when the objective function is obtained through a finite element analysis, due, for example, to the absence of an analytical solution of the problem, and the direct use of a stochastic method would be unfeasible for practical purposes, because of the intolerable processing time required. Copyright © 2001 John Wiley & Sons, Ltd.

show abstract

A combined strategy for optimization in nonlinear magnetic problems using simulated annealing and search techniques

Cited by 51 publications

References 4 publications

Compact MRI Magnet Design by Stochastic Optimization

Compact MRI Magnet Design by Stochastic Optimization

A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices

An efficient hybrid algorithm for the optimization of problems with several local minima

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