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A new Fruit Fly Optimization Algorithm: Taking the fifinancial distress model as an example 2 E# s4 H/ ~% Z" s2 f# g/ [% b" q* i
+ a: a; y- g& yThe treatment of an optimization problem is a problem that is commonly researched and discussed by" B6 h: B* n" l" _3 k
scholars from all kinds of fifields. If the problem cannot be optimized in dealing with things, usually lots( H& `9 ^; S9 Q, O7 \; G# [% t
of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted* q6 G0 l% ~- | Z2 U' m
efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with
6 u3 T% Z( B d. z/ d7 m* D$ W) Sthe complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization
- I8 f, R9 g v( y2 qAlgorithm. In this article, throughout the process of fifinding the maximal value and minimal value of a
* a& ]6 @$ G: c% Q6 bfunction, the function of this algorithm is tested repeatedly, in the mean time, the population size and
) t, J4 N) a3 b7 B6 P% fcharacteristic is also investigated. Moreover, the fifinancial distress data of Taiwan’s enterprise is further
! @0 l0 s- H; A9 Wcollected, and the fruit flfly algorithm optimized General Regression Neural Network, General Regression
, i9 {+ f* w) p; _( SNeural Network and Multiple Regression are adopted to construct a fifinancial distress model. It is found in' ?3 i! l% v" ]" X
this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression* O8 K: Z1 c( `, q0 b& d
Neural Network model has a very good convergence, and the model also has a very good classifification" a" X0 F$ H- H( }' i: B- f
and prediction capability.( W2 G5 o& E; O7 ]
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