A Hybrid Meta-Heuristic Algorithm based on Imperialist Competition Algorithm

The human has always been to up to the best in everything, and this perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables to find the best acceptable answer to the limitations in a problem, so that the objective function is a minimum or a maximum. Metaheuristic algorithms are one of inaccurate optimization methods that inspired by nature. In the recent years, much effort has been made to improve or create metaheuristic algorithms. One of the ways available to make improvements in meta-heuristic methods is to use combination. In this paper, a hybrid optimization algorithm is presented based on the imperialist competitive algorithm (ICA). The ideas used in ICA are an assimilation operation with a variable parameter and a war function that is based upon the mathematical model of a war in the real world. These changes lead to an increase in speed, find a global optimum, and reduce the search steps in contrast with the other meta-heuristic algorithms, so that the evaluations are made for more than 80% of the test cases. The proposed algorithm superior to the imperialist competitive algorithm, social based algorithm, cuckoo optimization algorithm, and genetic algorithm.