BABC: A Binary Version of Artificial Bee Colony Algorithm for Discrete Optimization

The Artificial Bee Colony (ABC) algorithm recently gained high popularity by providing a robust and efficient approach for solving continuous optimization problems. In order to apply ABC in discrete landscape, a binary version of artificial bee colony (BABC) algorithm is proposed in this manuscript. Unlike the original ABC algorithm, the proposed BABC represents a food source as a discrete binary variable and applies discrete operators to change the foraging trajectories of the employed bees, onlookers and scouts in the probability that a coordinate will take on a zero or one value. With four mathematical benchmark functions, BABC is proved to have significantly better performance than the other two successful discrete optimizer, namely the genetic algorithm (GA) and particle swarm optimization (PSO).