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EDA365欢迎您登录!您需要 登录 才可以下载或查看,没有帐号?注册  A new Fruit Fly Optimization Algorithm: Taking the fifinancial distress model as an example$ F: O" n" P  N# O$ P5 V 
 6 T- M1 f. g8 J8 tThe treatment of an optimization problem is a problem that is commonly researched and discussed by
 / Y/ W# W" J7 rscholars from all kinds of fifields. If the problem cannot be optimized in dealing with things, usually lots: ?9 ~) v' d; c# q, n, |- J( o
 of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted  I, b! g% K% [4 m! N
 efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with
 6 c) ?4 g# V. K& pthe complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization
 % [$ Q# q4 Z( s* a' AAlgorithm. In this article, throughout the process of fifinding the maximal value and minimal value of a
 G2 p  m. R$ S2 v! u* ?' ^( R' A8 `function, the function of this algorithm is tested repeatedly, in the mean time, the population size and
 1 ^# }% `# h# \1 Q: G! h2 Gcharacteristic is also investigated. Moreover, the fifinancial distress data of Taiwan’s enterprise is further; K! x) W8 A2 [3 y! y
 collected, and the fruit flfly algorithm optimized General Regression Neural Network, General Regression  C3 n4 t, k, L3 h% C
 Neural Network and Multiple Regression are adopted to construct a fifinancial distress model. It is found in
 ( a; R' H4 G2 p! t; m7 P* Uthis article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression: {; Y, r0 h4 }5 v9 ?8 `' \
 Neural Network model has a very good convergence, and the model also has a very good classifification
 ( e5 r; L8 E" |- z. \and prediction capability.
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