A novel hybrid Chaotic Aquila Optimization algorithm with Simulated Annealing for Unmanned Aerial Vehicles path planning. (December 2022) - Ait-Saadi, Amylia; Meraihi, Yassine; Soukane, Assia; Ramdane-Cherif, Amar; Benmessaoud Gabis, Asma
Abstract: 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. Graphical abstract: Highlights: 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.