Imperialist Competitive Algorithm Using Chaos Theory for Optimization (CICA)

Abstract

The Imperialist Competitive Algorithm (ICA) that was recently introduced has shown its good performance in optimization problems. This novel optimization algorithm is inspired by socio-political process of imperialistic competition in the real world. In this paper a new Imperialist Competitive Algorithm using chaotic maps (CICA) is proposed. In the proposed algorithm, the chaotic maps are used to adapt the angle of colonies movement towards imperialist’s position to enhance the escaping capability from a local optima trap. The ICA is easily stuck into a local optimum when solving high-dimensional multi-model numerical optimization problems. To overcome this shortcoming, we use four different chaotic map incorporated into ICA to enhance the exploration capability. Some famous unconstraint benchmark functions are used to test the CICA performance. Simulation results show this variant can improve the performance significantly