Abstract
The - similarity problem, finding a group of objects which have the most similarity to each other, has become an important issue in information retrieval and data mining. The theory of this concept is mathematically proven, but it practically has high time complexity. Binary Genetic Algorithm (BGA) has been applied to improve solutions quality of this problem, but a more efficient algorithm is required. Therefore, we aim to study and compare the performance of four metaheuristic algorithms called Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Imperialist Competitive Algorithm (ICA) and Fuzzy Imperialist Competitive Algorithm (FICA) to tackle this problem. The experiments are conducted on two applications; the former is on four UCI datasets as a general application and the latter is on the text resemblance application to detect multiple similar text documents from Reuters datasets as a case study. The results of experiments give a ranking of the algorithms in solving the -similarity problem in both applications based on the exploration and exploitation abilities, that the FICA achieves the first rank in both applications as well as based on the both criteria.