A Mutation Ant Colony Algorithm for the Generalized Assignment Problem under the Special Constraints

A generalized assignment problem under the special constraints is proposed, which can be solved by the ant colony algorithm (ACA). But the ACA for the problem is easy to fall in local optima because the constraints are relatively complicated. In this paper, the diversity in the results is maintained and the convergent speed is elevated by an adaptive method of updating pheromone and a mutation strategy for clearing up invalid assignment. The computer experiments demonstrate that the optimization performance is improved by the proposed algorithm, which is more suitable for the real-time application.