## 1 Introduction
[16] proposed a multi-strategy integrated bee colony algorithm, which utilizes different individual experiences with multiple search strategies to update the global path. [PERSON] [17] and [PERSON] [18] combined the bee colony algorithm with the ant colony algorithm to improve the quality of the initial solution, optimize the number of path turns, and avoid blind searches. [PERSON] et al. [19] added a virtual goal to the global search points to achieve fast local search optimization. [PERSON] et al. [20] divided the global search process into several segments using the water flow potential field method and optimized the local search coordinate points according to the obstacles' characteristics using the beetle algorithm to prevent falling into local optimal points. In addition, introducing chaotic functions and reverse learning mechanisms are the main strategies for improving the performance of swarm intelligence algorithms. [PERSON] et al. [21] and [PERSON] et al. [22] incorporated non-uniform variation and chaotic mapping functions into the sparrow search algorithm, respectively, which enhances the randomness and traversal ability of the populations. [PERSON] et al. [23] introduced a stochastic reverse learning mechanism into the golden jackal algorithm to overcome the limitations of balancing exploration and exploitation and improve the convergence speed. [PERSON] et al. [24] introduced variational and greedy strategies into the rabbit algorithm to improve the quality of spatial solution exploration and population exploitation. The aforementioned literature has improved the efficiency of algorithms in path planning by means of hybrid swarm intelligence algorithms, introducing optimization strategies, etc. However, there are still problems such as early convergence of global exploration, low local optimization accuracy, and high dependence on parameter settings. According to the No Free Lunch Theorem [25], no single intelligent optimization algorithm can universally solve all path planning problems. Even a well-optimized single algorithm fails to simultaneously address the dual challenges of global search and local optimization. To tackle this, the IABCBAS algorithm is tailored for path planning in multi-dimensional environments. This algorithm leverages swarm intelligence mechanisms for global exploration of path waypoints while employing the beetle algorithm for local path refinement. The main contributions of this study are summarized as follows. Firstly, Tent chaotic perturbation is employed to enhance bee population diversity. By non-uniformly adjusting the updated swarm positions, the algorithm broadens the distribution of solution sets, optimizes search point quality, and boosts convergence speed. Secondly, a random reversal strategy is adopted to increase search direction points, thereby expanding the optimization range of the beetle algorithm and strengthening its ability to escape local optima. Position-based greedy selection is employed to achieve higher convergence accuracy. Thirdly, an adaptive inertia weight factor is introduced to autonomously adjust the weight distribution between the two algorithms, thereby achieving a balanced trade-off between global search capability and local optimization performance. Fourthly, path planning simulation experiments are conducted; the swarm algorithm performs global path pre-search, while the beetle algorithm handles local optimization. Two-dimensional, three-dimensional, and real-world environments are established to verify the engineering application value of the proposed algorithm. This paper is structured into five parts. Section 1 provides a relevant introduction to the research content. Section 2 elaborates on the artificial bee colony algorithm, beetle swarm algorithm, and their respective improvement strategies. Section 3 focuses on testing the performance of the proposed algorithm. Section 4 presents path planning experiments conducted in 2D, 3D simulation environments, and a real-world environment. Section 5 includes conclusions and future prospects.