摘要：Since their introduction, bio-inspired algorithms, especially the ones based on the social behaviour of the animals that live in colonies have demonstrated great potential in finding near-optimal solutions for both unconstrained and constrained hard optimization problems. In this research, a parallel version of the popular Artificial Bee Colony (ABC) algorithm for optimization of constrained problems, has been introduced. An island-based model, in which the whole population is divided into subpopulations, is used. Subpopulations execute the serial version of the original algorithm and occasionally exchange the obtained results. The proposed algorithm has been tested based on a set of well-known constraint benchmark functions and five real-world engineering design problems. The results demonstrate clear improvements compared with those obtained with the original ABC algorithm.
