Abstract
Bat Algorithm (BA) and Artificial Bee Colony Algorithm (ABC) are frequently used in solving global optimization problems. Many new algorithms in the literature are obtained by modifying these algorithms for both constrained and unconstrained optimization problems or using them in a hybrid manner with different algorithms. Although successful algorithms have been proposed, BA’s performance declines in complex and large-scale problems are still an ongoing problem. The inadequate global search capability of the BA resulting from its algorithm structure is the major cause of this problem. In this study, firstly, inertia weight was added to the speed formula to improve the search capability of the BA. Then, a new algorithm that operates in a hybrid manner with the ABC algorithm, whose diversity and global search capability is stronger than the BA, was proposed. The performance of the proposed algorithm (BA_ABC) was examined in four different test groups, including classic benchmark functions, CEC2005 small-scale test functions, CEC2010 large-scale test functions, and classical engineering design problems. The BA_ABC results were compared with different algorithms in the literature and current versions of the BA for each test group. The results were interpreted with the help of statistical tests. Furthermore, the contribution of BA and ABC algorithms, which constitute the hybrid algorithm, to the solutions is examined. The proposed algorithm has been found to produce successful and acceptable results.
Highlights
Meta-heuristic algorithms are often used in the solution of optimization problems
When compared by the test line in the table, BA_ABC performed better in 22 functions compared to Bat Algorithm (BA), and a significant difference was found between them
The method we proposed was applied three engineering three engineering optimization problems, including pressure vessel to design problem, optimization problems, including pressure vessel design problem, tension/compression spring design tension/compression spring design problem, and gear train design problem, which are frequently problem, and gear train design problem, which are frequently used in literature, and its performance used in literature, and its performance was examined
Summary
Meta-heuristic algorithms are often used in the solution of optimization problems. These algorithms use natural phenomena to achieve a specific purpose. Cai et al [5] added the optimal forage and random disturbance strategy concepts to the algorithm to determine the search direction of bats and improve their global search capability. Zhu et al [8] proposed a quantum-behaved bat algorithm In this new algorithm, the position of each bat is determined by the optimal solution initially available, and by the mean best position, which can increase the convergence rate of the algorithm in the following iterations. Wang et al [16] proposed a novel bat algorithm that includes multiple strategies for speed and position determination formulas to overcome BA’s weakness in solving complex problems. Yıldızdan and Baykan [24] proposed a new algorithm using the hybrid differential evolution algorithm with an advanced BA algorithm to overcome the structural problems of BA and increase its exploration ability. In the seventh section, the conclusion and future work are explained
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