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Research For Genetic Algorithm And Its Application To The Optimal Design Of Electromagnetic Devices

Posted on:2007-05-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:T G ChenFull Text:PDF
GTID:1102360215995240Subject:Electrical theory and new technology
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The objective functions of optimal design of electromagnetic devices are often highly non-linear, abrupt, multi-hump, discontinuity or non-differential, and almost all the problems need huge computation that makes the traditional optimization techniques be incapable. The optimal design of transverse flux induction heating (TFIH) devices for continuously moving strip involves three-dimensional coupled problem of eddy field and temperature field and global optimization design problem.Based on the analysis of the reason for the premature convergence causing in genetic algorithm, and enlightened by the phenomenon of mutation and disaster in the biology evolution procession, an adaptive population disappearance genetic algorithm (APDGA) was presented and discussed. The diversity measure function was presented to describe the similar extent of population using the Hamming distance of each individual, and then educed disappear number of individual which vary from the diversity of population adaptively, the same number of new individuals are added to the population randomly to maintain the suitable population size and the diversity of population. Compared with standard genetic algorithm (SGA), APDGA can observably improve the distribution character of population, maintain population diversity, restrain premature phenomenon, decrease the dependence of SGA to the character of initial population, and has much highly convergence ratio. With elitist strategy its convergent speed has great increase. APDGA is a high performance Genetic Algorithm.Inspired by the phenomenon of genetic recombination and inversion operator, a cyclic shift genetic algorithm (CSGA) was presented and discussed with different strategy. Based on the SGA, implements cyclic shift operator with different strategy to make the gene code of individual reformed, and generates new individual. From the results of simulation, the average convergent generations of CSGA are small than that of SGA. CSGA can effectively suppress the premature phenomenon, and enhance the convergent speed and rate.APDGA and CSGA were successfully used in the optimal design of electromagnetic relay. According to the macro strategy of GA and design idea of Hybrid Genetic Algorithm (HGA), the new structure of HGA– embed structure was discussed. The structure takes the PSO as a operator, and add it to the SGA to form new hybrid genetic algorithm called HGAPSO. From the simulation results, HGAPSO has better searching quality and efficiency than that of SGA or PSO, and is applied successfully to the temperature neural network (NN) prediction in transverse flux induction heating (TFIH).The paper discussed the optimal design problem of TFIH devices for continuously moving strip finally. Firstly the mathematical simulation of the problem was done by Ansoft software, and founded the relation between the form and size of coil and the heat resource distribution at the device outlet. Secondly built the neural network model of temperature prediction to save the calculative time and difficulty of the work, and fulfilled the NN sample train by taking the simulation results as the samples. Finally founded math model of optimal design of TFIH device for continuously moving strip with three variables as frequency,current and ratio between coil outside radius and strip width, and obtained the optimal results of the math model by HGAPSO and the NN.
Keywords/Search Tags:genetic algorithm (GA), adaptive population disappearance genetic algorithm (APDGA), cyclic shift genetic algorithm (CSGA), hybrid genetic algorithm, particle swarm optimization (PSO), transverse flux induction heating (TFIH), couple, neural networks
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