Abstract
In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cultivation process model is considered. The computational results of the designed algorithm are compared to the results of different hybridized biologically inspired techniques (ant colony optimization (ACO) and firefly algorithm (FA))—hybrid algorithms as ACO-GA, GA-ACO and ACO-FA. The algorithms are applied to the same problems—a set of benchmark test functions and the real nonlinear optimization problem. Taking into account the overall searchability and computational efficiency, the results clearly show that the proposed ABC–GA algorithm outperforms the considered hybrid algorithms.
Highlights
Many different methods of global optimization have been developed, the efficiency of an optimization method is always determined by the specific nature of the particular problem
It can be noticed that artificial bee colony (ABC)-genetic algorithms (GA) has a relatively fast convergence toward its final optimal value compared to the other hybrid algorithms
Terms of convergence, it can be noticed that ABC-GA has a relatively fast convergence toward its final optimal value compared to the other hybrid algorithms
Summary
Many different methods of global optimization have been developed, the efficiency of an optimization method is always determined by the specific nature of the particular problem. This literature review shows that, as a result of the ABC hybridization, the algorithm’s performance is improved, and optimal solutions to different problems are achieved. This fact, and the known successful applications of GA, for parameter identification of nonlinear models of cultivation processes, inspired the authors to work on the development of a new hybrid metaheuristic algorithm between ABC and GA. The proposed hybrid algorithm in this paper, named ABC-GA, is designed to improve both the exploration and exploitation and present a powerful and efficient algorithm for real-world numerical optimization problems. The performance of the proposed ABC-GA hybrid algorithm is examined with two different test groups, including classic benchmark test functions and a real nonlinear optimization problem.
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