Abstract
This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is added to the position update, so that the algorithm maintains a good population diversity and the global search ability is improved; and the speed adjustment factor is added, which effectively improves the speed and accuracy of the algorithm. The proposed algorithm was then tested using 10 benchmark functions and 2 classical engineering design optimizations. The simulation results show that the LAFBA has stronger optimization performance and higher optimization efficiency than basic bat algorithm and other bio-inspired algorithms. Furthermore, the results of the real-world engineering problems demonstrate the superiority of LAFBA in solving challenging problems with constrained and unknown search spaces.
Highlights
Many problems in management can be treated as global optimization problems, and the need to efficiently solve large-scale optimization problems has prompted the development of bio-inspired intelligent optimization algorithms
In order to improve the performance of the bat algorithm, this paper proposes an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA)
Observing the convergence curves of the corresponding functions, the inflection point in the curve shows that the LAFBA algorithm successfully jumps out of the local optimum and continues to optimize, while the other algorithms converge to the local optimum too early, resulting in a higher curve than the LAFBA
Summary
Many problems in management can be treated as global optimization problems, and the need to efficiently solve large-scale optimization problems has prompted the development of bio-inspired intelligent optimization algorithms. The bat algorithm has the advantages of having less setting parameters, being easy to understand and implement, and having fast convergence It has drawbacks in balancing global and local search capabilities, it is easy to fall into a local optimum, and the solution accuracy is not high. To overcome these shortcomings, many scholars have made improvements to the bat algorithm. Ramli and other scholars, in order to improve the global search ability of the bat algorithm, put forward an enhanced bat algorithm (MBA) based on dimensional and inertia weight factor to enhance the convergence [12]. The results obtained show that LAFBA is competitive in comparison with other state-of-the-art optimization methods
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