An improved artificial bee colony algorithm for minimal time cost reduction

The artificial bee colony (ABC) is a popular heuristic optimization algorithm. Although it has fewer control parameters, it shows competitive performance compared with other population-based algorithms. The ABC algorithm is good at exploration, but poor at exploitation. Recently, a global best-guided ABC (GABC) algorithm, inspired by particle swarm optimization, has been developed to tackle this issue. However, GABC cannot be applied to binary optimization problems. In this paper, we develop an improved ABC (IABC) algorithm with a new food source update strategy. IABC employs information about the global best solution as well as personal best solutions, thus enhancing the local search abilities of the bees. The new algorithm is adjusted to solve the binary optimization problem of minimal time cost reduction. We conduct a series of experiments on four UCI datasets, and our results clearly indicate that our algorithm outperforms the existing ABC algorithms, especially on the medium-sized Mushroom dataset.