Novel Memetic Algorithm implemented With GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search) for Optimal Design of Electromagnetic System

Ahn, Youngjun; Park, Jiseong; Lee, Cheol-Gyun; Kim, Jong-Wook; Jung, Sang-Yong

doi:10.1109/tmag.2010.2043228

Cited by 53 publications

(18 citation statements)

References 9 publications

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“…When the P c value is small, exploration can stop in the minimum region. In this scenario, we used an alternative approach to an evaluation of population selection assessment using simple and robust classifiers, previously defined by Ahn et al When the P m value is too much, GA can work as random search algorithm. When the P c and P m values are large then AGA is to select.…”

Section: Proposed Egsmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

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Section: Proposed Egsmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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“…GA have been widely used in optimization of electrical machine design [3], [6], [7], however, other evolutionary algorithms, like Immune Algorithms, Evolution Strategy, Differential Evolution [8], [9] and Particle Swarm Optimization [10] also show good results. In many cases, a hybrid solution is employed, where an Evolutionary Algorithm is used to find the area of the global minimum, and a local search algorithm is used to find the precise location of the minimum [4], [11], [12]. This reduces the number of evaluations drastically.…”

Section: B Previous Workmentioning

confidence: 99%

Gradient based optimization of Permanent Magnet generator design for a tidal turbine

Røkke

2014

2014 International Conference on Electrical Machines (ICEM)

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Abstract-An optimization of an analytical problem with nine variables is executed to find the optimal Permanent Magnet(PM) generator for a tidal turbine. A gradient based solver is used to find the minimum cost of active materials for the given design specifications. The MATLAB function fmincon is used, and the possible minimization algorithms available for this function are compared. As these solvers are only able to find a local minimum, a search is performed trying to find other minimas, both using a MultiStart procedure and using a Genetic Algorithm (GA). Losses are calculated for windings, stator laminations and rotor magnets and solid steel, and a constraint is put on efficiency. The cost effect of varying this constraint is investigated. Optimizations are done with both weight and material cost as objective function, and the different resulting designs are presented.

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“…The coefficients j a and the polynomial g are determined by the interpolation conditions. A GA is utilized to solve the optimization problem [27]. Using an approximation model, the optimum design can increase the accuracy of the optimal results more than by only using a GA because the approximation model is suitable for developing a nonlinear model.…”

Section: Objectivementioning

confidence: 99%

“…Using these results, an approximation model is made using a radial basis function (RBF) [26]. Then the optimization results are obtained using a genetic algorithm (GA) [27]. The validity of the presented optimum design is verified through experimental results.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of a Single-Phase Line-Start PM Motor Considering Efficiency, Maximum Torque, and Starting Torque

Baek

Kwon

2012

IEEE Trans. Magn.

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In this paper, an optimum design for a single-phase line start permanent magnet (LSPM) motor with respect to efficiency, maximum torque, and starting torque is proposed. The design variables, objective functions, and constraints are selected for this optimum design, which is divided into three steps.Step I maximizes the rated efficiency while maintaining a balanced operation and minimizing the stator copper loss.Step II utilizes equivalent circuit analysis to consider the efficiency and the maximum torque, while step III calculates the precise starting torque using finite element (FE) analysis. The validity of the optimum design is verified by comparing the efficiency, maximum torque, and starting torque characteristics of the time-step FE analysis with experimental results.

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Novel Memetic Algorithm implemented With GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search) for Optimal Design of Electromagnetic System

Cited by 53 publications

References 9 publications

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Gradient based optimization of Permanent Magnet generator design for a tidal turbine

Optimum Design of a Single-Phase Line-Start PM Motor Considering Efficiency, Maximum Torque, and Starting Torque

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