PICA: Multi-population Implementation of Parallel Imperialist Competitive Algorithms

The importance of optimization and NP-problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that are mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared-memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.