The beetle antennae search (BAS) algorithm is a new meta-heuristic algorithm which has been shown to be very useful in many applications. However, the algorithm itself still has some problems, such as low precision and easy to fall into local optimum when solving complex problems, and excessive dependence on parameter settings. In this paper, an algorithm called beetle antennae search algorithm based on Lévy flights and adaptive strategy (LABAS) is proposed to solve these problems. The algorithm turns the beetle into a population and updates the population with elite individuals' information to improve the convergence rate and stability. At the same time, Lévy flights and scaling factor are introduced to enhance the algorithm's exploration ability. After that, the adaptive step size strategy is used to solve the problem of difficult parameter setting. Finally, the generalized opposition-based learning is applied to the initial population and elite individuals, which makes the algorithm achieve a certain balance between global exploration and local exploitation. The LABAS algorithm is compared with 6 other heuristic algorithms on 10 benchmark functions. And the simulation results show that the LABAS algorithm is superior to the other six algorithms in terms of solution accuracy, convergence rate and robustness.
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