Cost optimization of salient pole synchronous machine by a new approach to the genetic algorithm

Genetic Algorithms appeared in literature since 1970. Lots of articles and books at different departments and subjects on this topic are published. In this issue, the weight minimization of the machine is done with GA, which is one of the optimization techniques applied to the electric machines, the results are determined due to the new GA - that is the main topic of this issue - and compared with the results of classic GA. Mendelian analyze of Genetics are based to the new GA, the concept which was known as the population genetics of Mendel, is applied to the algorithm. Consequently some different techniques are shown, the crossover operation became more feasible, a different reproduction way is proposed and mutation is not applied. The first rule of Mendel is known as: the segregation of Alleles and the second: the independent assortment of Alleles. So that, in spite of the crossover operation that is not known completely in medical science (anyone know the reason of creation of crossover and which segment of chromosome will be crossed over), and also the mutation operation that is an abnormal situation in medical science (and in spite of mutation have a very little probability), the reduction and increment of frequency of mutation are very normally operated in classic GA and non-satisfactory case of randomization of crossing over and of the crossover methods etc. are discarded with the new GA. While each one of the number string is called chromosome in classic GA, each of this number string is called "gamete" in the new GA, and the combination of the two gametes (one from male and one from female) is created a new "individual'' (as in the medical science literature). Afterwards, "genotype" and "phenotype" concepts are applied on this individual (Mendelian analyze of inheritance). In the respect of this, the creation of every individual from two gametes is applied to the new algorithm. While new generation is created with the randomly produced initial population, with reproduction which was based on roulette wheel selection method, with crossover and with mutation routines, respectively in classic GA, the new generation is created with the same initial population (gamete population), with the determination of Punett square for crossover, and with selection of one of these genotypes with roulette wheel selection method routines respectively in the new GA. Moreover, the generation number is less effective in the new GA the classic GA. In this task an analytically designed 2 MVA synchronous machine is optimized by classic and new GA. The chromosome (or gamete) of the machine contains 11 parameters. While the sub population number is fixed, the initial population number is changed and the results are determined for different selection methods beyond the increment of generation number in classic GA. While the initial population number and the sub population number are changed, the generation number is not important (the generation number depends on the sub population number in the new GA), the results are determined on this way. The results of classic and new GA are compared. Key Words: Genetic Algorithm, Mendelian Genetic, Optimization, Synchronous Machine, Electric Machine. xvi