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A new Fruit Fly Optimization Algorithm: Taking the fifinancial distress model as an example 6 t9 K3 S' k; @6 ~, _3 n
! `7 S5 E8 K1 W0 X! ]0 T9 S" PThe treatment of an optimization problem is a problem that is commonly researched and discussed by
6 b6 R8 L! W) O2 F5 _scholars from all kinds of fifields. If the problem cannot be optimized in dealing with things, usually lots8 x& R& I! F/ }- j) z
of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted0 O* a7 S d* K* W6 [7 W) y4 r; q7 C
efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with; C. [9 G+ y, v0 P
the complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization
8 X# u7 Q7 M! V8 jAlgorithm. In this article, throughout the process of fifinding the maximal value and minimal value of a
/ [5 `# l9 S# d+ L$ I3 Pfunction, the function of this algorithm is tested repeatedly, in the mean time, the population size and
! ]) ]) q* m1 Qcharacteristic is also investigated. Moreover, the fifinancial distress data of Taiwan’s enterprise is further4 a. o! F4 x- c( L. {5 R5 O
collected, and the fruit flfly algorithm optimized General Regression Neural Network, General Regression
% C8 i ?; \, p" }! zNeural Network and Multiple Regression are adopted to construct a fifinancial distress model. It is found in
( l% J: Z7 I) W7 a0 Y qthis article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression; Q" s% U, b9 X/ v) ]+ O; j$ v' c
Neural Network model has a very good convergence, and the model also has a very good classifification
$ ~5 o+ G3 [) ^( [; nand prediction capability.
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