6. Imperialist Competitive Algorithm

This chapter presents imperialist competitive algorithm (ICA) proposed by Atashpaz-Gargari and co-workers [1, 2] which is a socio-politically motivated optimization algorithm. ICA was firstly used by Kaveh and Talatahari [3] for steel structural optimization. Like other population-based metaheuristics, ICA starts with a set of random initial candidate solutions, as the initial countries. A specific number of best countries are considered as the emperors which take a number of remaining countries as their colonies based on competency. Therefore, the countries are categorized into emperors and colonies and collectively form empires or imperialist states. ICA has two main mechanisms who are the basis of the ICA: improving the colonies of each empire by intrinsic learning of colonies from their emperor and imperialistic competitions among empires. First one results in powering empires themselves. In this way each colony has opportunity to take the role of emperor of that empire. During imperialistic competitions among empires, weakest empires lose their weakest colonies, and powerful empires take possession of them until the weakest empire collapses. The power of each imperialist or empire not only depends on the quality or position of its emperor and colonies but also depends on the number of its colony. Colonies improvements and imperialistic competitions direct the search process toward the powerful imperialists or the optimum points. These are repeated in the cyclic body of the algorithm in succession to satisfy a stopping criteria with the aim of collapsing all the empires except the most powerful one which will have all the countries under its control.