Mendeley
TY  _  JOUR
ID  -  20200418000000
TI  -  Modified orthogonal chaotic colonial competition algorithm and its application in improving pattern recognition in multilayer perceptron neural network
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  Moallem Payman
AU  -  sadeghi hariri mehrdad
AU  -  hashemi MAHDI
AD  -  هیات علمی گروه مهندسی برق
AD  -  دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
AD  -  موسسه آموزش عالی پیام گلپایگان، دانشکده مهندسی برق
Y1  -  2020
PY  -  2020
VL  -  35
IS  -  
SP  -  1
EP  -  14
KW  -  Orthogonal Chaotic Colonial Competition Algorithm
KW  -   Multilayer Perceptron Neural Network
KW  -   Data Classification
DO  -  
N2  -  Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm.
UR  - rimag.ir/en/Article/6561
L1  - rimag.ir/en/Article/Download/6561
ER  -  
