One of the most important and valuable goal of software development life cycle is software cost estimation or SCE. During the recent years, SCE has attracted the attention of researchers due to huge amount of software project requests. There have been proposed so many models using heuristic and meta-heuristic algorithms to do machine learning process for SCE. COCOMO81 is one of the most popular models for SCE proposed by Barry Boehm in 1981. However COCOMO81 is an old estimation model, it has been widely used for the purpose of cost estimation in its new forms. In this paper, the Imperialism Competition Algorithm (ICA) has been employed to tune the COCOMO81 parameters. Experimental results show that in the separated COCOMO81 dataset, ICA can estimate the COCOMO81 model parameters such that the performance parameters are significantly improved. The proposed hybrid model is flexible enough to tune the parameters for any data sets in form of COCOMO81. Manuscript profile