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 Orthog
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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.
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