Parallel optimization based on artificial bee colony algorithm
Debo Li, Yongxin Feng, Jun Zhong, Jielian Zhou, Libao Yin, Junhao Zhou · 2017
This paper aims to tackle the shortcomings of the standard artificial bee colony algorithm (ABC) such as slow convergence, long solving time and being easy to fall into local optima. We study the state transformation formula and propose a parallelized ABC algorithm with Message Passing Interface (MPI). We use the traveling salesman problem (TSP) as the case study. Our experiments show that the parallel ABC algorithm has an advantage in speed over the standard algorithm w.r.t. iterations and convergence speed.