Optimization algorithms iired by nature as optimization intelligence methods alongside classical methods have shown good success. Colonial Competitive Algorithm is a novel Meta-heuristic algorithm which is iired by socio-politically phenomenon rather than a natural phenomenon. Similar to other evolutionary algorithms that start with an initial population, CCA begins with initial empires. Any individual of an empire is called a country. There are two type of countries; colony and imperialist state that collectively from empires. Imperialistic competitions among these empires form the basis of the CCA. During this competition, weak empires collapse and powerful ones tale possession of their colonies. In this study, we try to improve the performance of Colonial Competitive Algorithm, in term of convergence, accuracy and speed, based on opposition concepts. The main idea behind opposition concept is the simultaneous consideration of an estimate and its corresponding opposite estimate in order to achieve a better approximation for the current candidate solutions. As an advantage of opposite versus random points, purely random re-sampling or selection of solutions from a given population, has a higher chance of visiting or even revisiting unproductive regions of the search space. As you know, finding the more accurate solution(s) in a shorter period of time for complex nonlinear problems, is the main goal of all optimization Meta-heuristics and still widely open to search. All of these fact encourage us to employ the opposition concept to accelerate optimization techniques. Move towards optimal solution based on opposition improves the chance of achieving a better solution and provides a more efficient direction to the algorithm
