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In recent years, research on Unmanned Aerial Vehicles has become one of the most interesting topics for industry and academics. UAVs path planning is one of the most important issues in terms of guaranteeing good performance in real-world applications. Its main objective is to determine and ensure an optimal and collision-free trajectory (path) between two positions from a starting point (source) to a destination point (target), while dealing with some requirements (e.g. safety, environment complexity, obstacle avoidance, energy consumption, etc.). In view of this topic's complexity, an efficient path planning algorithm is required. In this paper, we propose an improvement of the meta-heuristic African Vulture Optimization Algorithm (AVOA), named Chaotic Cauchy Opposition-based AVOA (CCO-AVOA), for solving the UAVs path planning problem in a 3D environment. The effectiveness of the proposed CCO-AVOA is validated in different environments with various numbers of waypoints and threats taking into account the fitness value, path cost, height cost, obstacles cost, UAV's angle cost, and execution time metrics. Compared to ten well-known meta-heuristics, simulation results demonstrate the efficiency of the proposed CCO-AVOA approach in most cases by obtaining a short, smooth, least costly, and collision-free path with better stability for UAVs in complex environments. • An improved algorithm (CCO-AVOA) is proposed for the UAV path planning. • The UAV path planning problem aims to minimize UAV requirements' costs. • The algorithms' performance is assessed across four different scenarios. • The CCO-AVOA algorithm proves superior in comparison with ten well-known algorithms. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles path planning is one of the critical issues in terms of guaranteeing good performance in real-world applications. Basically, it is responsible for determining and ensuring a short, smooth, and collision-free path between two positions from a source to a destination point. This paper presents a hybrid optimization scheme based on the hybridization of Chaotic Aquila Optimization with Simulated Annealing for solving the Unmanned Aerial Vehicles path planning problem. The main purpose of using Simulated Annealing is to provide a more suitable balance between exploitation/exploration. The performance of the proposed algorithm is assessed using three benchmark maps with various numbers of threats. Compared to nine well-known meta-heuristics, the results of simulated experiments demonstrate that the proposed algorithm performs better in the majority of the scenarios by obtaining a significant enhancement in minimizing the fitness value and the path cost up to 36% and 19%, respectively. [Display omitted] • A hybrid Chaotic Aquila Optimizer with Simulated Annealing algorithm (CAOSA) is proposed for UAV path planning. • The UAV path planning problem is formulated as a multi-objective function that aims to minimize the costs related to Path length, UAV's axes, Obstacle avoidance, and Altitude. • Computational experiments are performed on three different benchmark maps considering the fitness value, path cost, and execution time. • The superiority of the CAOSA algorithm is proved compared to nine well-known state-of-art algorithms. [ABSTRACT FROM AUTHOR]