Hybrid GA-ACO Algorithm for a Model Parameters Identification Problem
Stefka Fidanova, Marcin Paprzycki, Olympia Roeva
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 413–420 (2014)
Abstract. In this paper, a hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is introduced. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as - but usually better than - the best solution devised by GA. To demonstrate the usefulness of the presented approach, the hybrid scheme is applied to parameter identification of E. coli MC4110 fed-batch fermentation process model. Moreover, a comparison with both the conventional GA and ACO is presented. The results show that the hybrid GA-ACO takes the advantages of both GA and ACO, thus enhancing the overall search ability and computational efficiency.