## 1 Introduction
[22] proposed a new Double-Dynamic Biogeography-Based Learning Particle Swarm Optimization (DDBLPSO) Algorithm to overcome the shortcomings of basic PSO, which is inefficient and easy to fall into local optimum and finally applied the proposed algorithm to six kinds of terrain functions of UAV path planning including the city, village without houses, village with houses, mountainous area without houses, mountainous area with houses, mountainous area with houses, and mountainous area with a huge building. Finally, the result of the proposed algorithm is compared with four other related algorithms to verify the superiority of the proposed algorithm. To acquire an optimal and viable path in a complex environment for UAVs, [PERSON] et al. [1] proposed a Golden Eagle Optimizer with double learning strategies (GEO-DLS). The double learning strategies consist of personal example learning and mirror reflection learning, which enhance the search ability and convergence accuracy of the GEO algorithm, respectively. [PERSON] et al. [2] proposed an improved sparrow search algorithm named CASSA to solve the path optimization problem in complex 3D environments. The chaotic strategy and the Cauchy-Gaussian mutation strategy are introduced into the original SSA to enhance the population diversity and the stagnation resistance of the algorithm, respectively. Based on experimental results in the same environment, the modified CASSA has superiority over the basic SSA and other meta-heuristic algorithms in solving the UAV path planning problem. In this study, we proposed an improved hybrid Salp Swarm Algorithm (SSA) [16] and Aquila Optimizer (AO) [23] named IHSSAO for UAV path planning in complex terrain. Both SSA and AO are new meta-heuristic algorithms proposed in recent years. Especially, the AO has been proposed for such a short time that few scholars have improved this algorithm. Currently, because of its strong exploration capability and robustness, the AO has been modified by a few scholars to apply it to problems such as oil production forecasting [24], population forecast [25], task scheduling [26], and so on. For the moment, it has not been applied to the path planning of autonomous intelligent unmanned systems. Nevertheless, AO also has disadvantages, such as weak exploitation capability and easily falling into local optimum, which need to be further optimized. The SSA has a strong exploitation capability. The SSA has been applied to some optimization problems due to its strong exploitation capability [27]. Therefore, we aspire to propose a hybrid algorithm, which combines the advantages of SSA and AO. To further improve the proposed algorithm, we introduce the tent chaotic map and pinhole imaging opposition-based learning (PIOBL) strategies into the proposed algorithm. The main contributions of this paper are summarized as follows:  
* We integrate the leader mechanism of SSA into the position update formulation of the AO, which enables the search individuals to fully utilize the information of the optimal solution and enhances the global search capability of the proposed algorithm. * Tent chaotic map helps the IHSSAO to increase the original population diversity. * PIOBL helps the proposed algorithm to increase the original population diversity and the capability to escape from local optima. * To verify the performance of the proposed IHSSAO, we tested it against the SSA, AO, and five other advanced meta-heuristic algorithms by 23 classical benchmark functions and 17 IEEE CEC2017 test functions. * Eventually, we applied the IHSSAO, SSA, and AO to the UAV path planning problem. The experimental results verify that the proposed IHSSAO is superior to the basic SSA and AO in solving the path planning problem for UAVs in complex terrain. The rest of this paper is organized as follows. Section 2 presents the background knowledge of basic SSA and AO, as well as tent chaotic map and pinhole imaging opposition-based learning strategies. Section 3 provides a detailed description of the proposed IHSSAO.