A discrete collaborative swarm optimizer for resource scheduling problem in mobile cellular networks

This study proposes a discrete collaborative swarm optimizer (DCCSO) for solving the resource scheduling problem in mobile cellular networks, which aims to employ minimum wireless bandwidth to meet various channel demands from each cell without violation of interference constraint. Many current algorithms can provide satisfactory solutions in dealing with simple problems, while some complex problems still need efficient scheduling schema, due to the limited resources. The proposed algorithm is inspired by the competitive swarm optimizer, whose superiority on continuous optimization problems has been proven by theory and verification. With the characteristics of the resource scheduling problem, the generalized order learning mechanism is designed, which updates the information of the loser particles by learning the sequential knowledge of the winners. Besides, plenty of invalid solutions will generate during the searching process in the original solution space degeneration of the exploration capability and coverage speed. To that end, an ensemble self-learning strategy is arisen by helping the neighborhood search by problem-specific information in the transformed solution space. The effectiveness of the proposed DCCSO is demonstrated on a set of real-world problems, and the experimental results show that the proposed algorithm exhibits better than or at least comparable performance to other state-of-the-art algorithms on most problems.