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ABSTRACT
In this paper, a hybrid scheme using Bat Algorithm (BA) and Sequential Quadratic Programming (SQP) method is introduced. In the hybrid BA—SQP, the role of BA is to generate feasible solutions to a problem. The role of SQP is to exploit the information gathered by BA. This process obtains a solution which is at least as good as—but usually better than—the best solution devised by BA. To demonstrate the usefulness of the presented approach, the hybrid scheme was applied to parameter identification of an E. coli MC4110 fed-batch cultivation process model. A comparison with both the conventional BA and SQP method is presented. The results showed that the hybrid BA-SQP has the advantages of both BA's global search ability and SQP's local search ability, thus enhancing the overall search ability and computational efficiency. For comparison, the results obtained by applying Ant colony optimization algorithm in conditions similar to those of BA are further shown.
