Abstract
Artificial Bee Colony algorithm, inspired by the foraging behavior of real honey bees, is one of the most important swarm intelligence based optimization algorithms. Like other population based evolutionary computation techniques, Artificial Bee Colony algorithm is suitable for parallelization on distributed architectures. In this paper, we presented a new emigrant creation strategy that is being distributed between subcolonies running simultaneously on the independent compute nodes. The running times and objective function values obtained by the parallelized Artificial Bee Colony algorithm with the proposed model on different number of compute nodes are compared with the sequential counterpart of the algorithm and it is seen that convergence performance of the parallelized Artificial Bee Colony algorithm is significantly improved with the proposed emigrant creation strategy.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
