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A new Fruit Fly Optimization Algorithm: Taking the fifinancial distress model as an example + R- M2 \; [* J0 ?3 j. t' b
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The treatment of an optimization problem is a problem that is commonly researched and discussed by
3 _7 O3 X$ l) E# c' Q8 Wscholars from all kinds of fifields. If the problem cannot be optimized in dealing with things, usually lots1 e2 \( }8 t2 D6 }! j+ {8 c
of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted
. s. k+ l5 o2 R a6 m9 q2 Fefforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with
4 i# a$ X" t; H# T% Dthe complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization2 ~6 E, A& C1 _6 o
Algorithm. In this article, throughout the process of fifinding the maximal value and minimal value of a! h4 B) c- _8 n) ~! {
function, the function of this algorithm is tested repeatedly, in the mean time, the population size and* c. B- F7 l$ e; C# f
characteristic is also investigated. Moreover, the fifinancial distress data of Taiwan’s enterprise is further
4 f) W7 Y; Z# e# }) x$ icollected, and the fruit flfly algorithm optimized General Regression Neural Network, General Regression
6 v; U6 W x! m1 ]6 ~Neural Network and Multiple Regression are adopted to construct a fifinancial distress model. It is found in, d$ A# H, S# _/ _! @
this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression' J# z. T" ]; h3 X
Neural Network model has a very good convergence, and the model also has a very good classifification/ w0 D* ]& |7 ]8 M; }8 J o) o
and prediction capability.
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