Application of Hybrid Swarming Algorithm on a UAV Regional Logistics Distribution

Abstract

This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism for the first time. The Van der Waals force is applied to the pheromone updating mechanism. Simulation results show that the improved algorithm has advantages in quality and speed of solution compared with other mainstream algorithms. This paper provides fast and accurate route methods for solving the Traveling Salesman Problem first and a delivery scheme is also presented for UAVs to realize “contactless delivery” to users in the Changchun Mingzhu District during the COVID-19 epidemic, which confirms the practicability and robustness of the algorithm.

Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV

“…The maximum takeoff weight of the UAV in this article is 2 kg; the thrust of the selected brushless motor is 1.47 kg, and the wing area is 3400 cm 2 . From this, it can be calculated that the maximum wing load at takeoff is 65 g/dm 2 , and the takeoff thrust-to-weight ratio is 1.5.…”

“…With the development of drones in recent years, drone technology has had wide applications in multiple fields [1], such as civilian aerial photography and videography, mapping and surveying, agricultural production, logistics and express delivery [2], disaster monitoring and rescue, scientific research, and environmental sensing. Drones are characterized by their simple structure, light weight, low cost, easy operation, and high safety [3] and can be categorized into fixed-wing and multi-rotor types [4].…”

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

“…The MH algorithms are classified into two categories: non-nature-inspired and natureinspired. The categorization of natural-inspired meta-heuristics encompasses four main groups: biologically inspired algorithms (BIA), physics-based algorithms (PBA), humanbased algorithms (HBA), swarm intelligence (SI) algorithms, evolutionary algorithms (EA), and a miscellaneous category for those that do not fit into the groups mentioned above due to their diverse sources of inspiration, such as societal and emotional aspects [13,14]. Nature-based optimization approaches have experienced a process akin to the process of selection and elimination, resulting in their tendency to exhibit greater conciseness and superior performance compared to conventional techniques.…”