Hybrid GA-ACO Algorithm for a Model Parameters Identification Problem

Stefka Fidanova, Marcin Paprzycki, Olympia Roeva

DOI: http://dx.doi.org/10.15439/2014F373

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)

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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.