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A new Fruit Fly Optimization Algorithm: Taking the fifinancial distress model as an example ' ~3 P( m8 G3 z7 A( U; z, S
- v1 m5 }6 S% o( G2 w) X$ UThe treatment of an optimization problem is a problem that is commonly researched and discussed by
- i% x# s Y! f# F6 O! Cscholars from all kinds of fifields. If the problem cannot be optimized in dealing with things, usually lots
2 U# k7 A5 e$ @+ A5 F2 v" ?of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted7 i) l f: P0 o
efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with: `: h; r6 {; Z# Y
the complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization; R3 I O* s; @- w, z
Algorithm. In this article, throughout the process of fifinding the maximal value and minimal value of a
. {9 L- M c# `$ L7 @function, the function of this algorithm is tested repeatedly, in the mean time, the population size and
( n4 g2 S+ p& u0 wcharacteristic is also investigated. Moreover, the fifinancial distress data of Taiwan’s enterprise is further2 a3 V- i% H9 R1 l) _% Y& J
collected, and the fruit flfly algorithm optimized General Regression Neural Network, General Regression, o, C" [- |8 o' S9 Q
Neural Network and Multiple Regression are adopted to construct a fifinancial distress model. It is found in
/ D$ C. |5 }. D [* Q. p8 p! e0 V, Xthis article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression0 {2 c- D* }+ b! {& _, T0 v( T
Neural Network model has a very good convergence, and the model also has a very good classifification: { m1 E \* e6 H& s$ b8 b5 l0 S9 N
and prediction capability.6 O/ N% n1 \# {" c$ u
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