A hybrid metaheuristic for the cyclic antibandwidth problem

Our method hybridizes the artificial bee colony methodology with tabu search to obtain high-quality solutions in short computational times. Artificial bee colony is a recent swarm intelligence technique based on the intelligent foraging behavior of honeybees. The performance of this algorithm is basically determined by two search strategies, an initialization scheme that is employed to construct initial solutions and a method for generating neighboring solutions. On the other hand, tabu search is an adaptive memory programming methodology introduced in the eighties to solve hard combinatorial optimization problems. Our hybrid approach adapts some elements of both methodologies, artificial bee colony and tabu search, to the cyclic antibandwidth problem. In addition, it incorporates a fast local search procedure to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown with respect to the current state-of-the-art algorithm for this problem.