A combined strategy for optimization in nonlinear magnetic problems using simulated annealing and search techniques (original) (raw)

Global optimization for discrete magnetostatic problems

IEEE Transactions on Magnetics, 1992

In this paper the problem of global optimization o f magnetic structures composed of solenoids is faced, using a modified Simulated Annealing algorithm able to deal with the functions of continuous andlor discrete variables. The algorithm is tested using a discrete problem which allows determination of the cost function for every possible configuration of the system, providing information about the pattern of the cost function with respect to design variables. Despite the difficulty of minimizing the function, the proposed algorithm was able to locate the global minimum, or a point where the cost function has a value very close to it, seven times out of a total of ten runs. The algorithm used is described and results are presented and discussed.

A Novel Orthogonal Simulated Annealing Algorithm for Optimization of Electromagnetic Problems

IEEE Transactions on Magnetics, 2004

We propose a novel orthogonal simulated annealing algorithm (OSA) for optimization of electromagnetic problems. The algorithm performs best when it employs an intelligent generation mechanism (IGM) based on orthogonal experimental design (OED). The OED-based IGM can efficiently generate a good candidate solution for the next step by using a systematic reasoning method instead of the conventional method of random perturbation. We show empirically that OSA is more efficient in solving parametric optimization problems and in designing optimal electromagnetic devices than some existing optimization methods using simulated annealing algorithms and genetic algorithms.

Multiobjective optimization in magnetostatics: a proposal for benchmark problems

IEEE Transactions on Magnetics, 1996

Abslruct-A proposal for benchmark problems to test electromagnetic optimization methods, relevant to multiobjective optimization of a Solenoidal Superconducting Magnetic Energy Storage with active and passive shielding is presented. The system has been optimized by means of different optimization procedures based on the Global Search Algorithm, Evolution Strategies, Simulated Annealing and the Conjugate Gradient Method, all coupled to integral or Finite Element codes. A comparison of results is performed and the features of the problem as a test of optimization procedures are discussed.'

BMOA: Binary Magnetic Optimization Algorithm

Abstract—Recently, the behavior of natural phenomena has become one the most popular sources for researchers in to design optimization algorithms. One of the recent heuristic optimization algorithms is Magnetic Optimization Algorithm (MOA) which has been inspired by magnetic field theory. It has been shown that this algorithm is useful for solving complex optimization problems. The original version of MOA has been introduced in order to solve the problems with continuous search space, while there are many problems owning discrete search spaces. In this paper, the binary version of MOA named BMOA is proposed. In order to investigate the performance of BMOA, four benchmark functions are employed, and a comparative study with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is provided. The results indicate that BMOA is capable of finding global minima more accurate and faster than PSO and GA.

Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems

International Journal of Computer Mathematics, 2009

In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included.

A novel hybrid approach combining electromagnetism-like method with Solis and Wets local search for continuous optimization problems

Journal of Global Optimization, 2009

The electromagnetism-like method (EM) is a meta-heuristic algorithm utilizing an attraction-repulsion mechanism to move sample points towards optimality in continuous optimization problems. Traditionally, the EM uses two algorithms known as the original and revised EMs. This paper presents a novel hybrid approach for EM by employing a wellknown local search, called Solis and Wets. To show the performance of our proposed hybrid EM, a number of experiments are carried out on a set of well-known test problems and the related results are compared with two forgoing algorithms.

Shape optimization of nonlinear magnetostatic problems using the finite element method embedded in optimizer

IEEE Transactions on Magnetics, 1996

This paper describes a second-order algorithm using t h e method of Lagrange multipliers by penalizing t h e geometric constraints and the finite element field equations. The direction of optimization is determined by a newly developed closed form evaluation of the Hessian matrix. Reluctivity curves of nonlinear iron materials are taken into account and modeled by two types of exponential functions so that a t least their second-order derivatives are piece-wise continuous across t h e boundary of low and high magnetization regions. The algorithm has been tested on pole face optimization of a salient pole synchronous machine operating in saturation.

Optimal Design of Nonlinear Magnetic Systems Using Finite Elements

2004

An inverse finite element method was developed to find optimal geometric parameters of a magnetic device to approximate a desired magnetic flux density distribution at certain test points and directions selected in the device. The augmented Lagrange multipliers method was utilized to transform the constrained problem consisting of a least-square objective function and a set of constraint equations to the unconstrained problem. A second-order approach based on the Lagrange-Newton method was used to minimize the unconstrained problem to improve the design iteratively. Numerical calculation of derivatives in the second-order design sensitivity analysis becomes a difficult task if saturation in material properties is accounted. A novel approach is developed to minimize the computational effort by directly combining the optimization process with the nonlinear finite element equations. The best capabilities to parametrize the device geometry and to model the nonlinear material characteris...

Strategies for balancing exploration and exploitation in electromagnetic optimisation

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2013

PurposeElectromagnetic design utilising finite element or similar numerical methods is computationally expensive, thus efficient algorithms reducing the number of objective function calls to locate the optimum are sought. The balance between exploration and exploitation may be achieved using a reinforcement learning approach, as demonstrated previously. However, in practical design problems, in addition to finding the global optimum efficiently, information about the robustness of the solution may also be important. In this paper, the aim is to discuss the suitability of different search algorithms and to present their fitness to solve the optimization problem in conjunction with providing enough information on the robustness of the solution.Design/methodology/approachTwo novel strategies enhanced by the surrogate model based weighted expected improvement approach are discussed. The algorithms are tested using a two‐variable test function. The emphasis of these strategies is on accu...