Adaptive Imperialist Competitive Algorithm (AICA)

The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by sociopolitical process of imperialistic competition of human being in the real world. In this paper, a new Adaptive Imperialist Competitive Algorithm (AICA) is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed dynamically to adapt the angle of colonies movement towards imperialist´s position. The ICA is easily stuck into a local optimum when solving high-dimensional multi-model numerical optimization problems. To overcome this shortcoming, we use probabilistic model that utilize the information of colonies positions to balance the exploration and exploitation abilities of the imperialistic competitive algorithm. Using this mechanism, ICA exploration capability will enhance. Some famous unconstraint benchmark functions used to test the AICA performance. Also, we use the AICA Algorithm to adjust the weights of a three-layered Perceptron neural network to predict the maximum worth of the stocks change in Tehran´s Bourse Market. Simulation results show this strategy can improve the performance of the ICA algorithm significantly.