Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub

• Published in: Mathematics (Basel) Vol. 8; no. 3; p. 333
• Main Authors: Pan, Jeng-Shyang, Song, Pei-Cheng, Chu, Shu-Chuan, Peng, Yan-Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Algorithms; drone logistics; Hubs; improved compact cuckoo search algorithm; location of drone logistics hub; Logistics; Mathematics; Optimization; Rural areas; sampling technology; Search algorithms; Search process; Supply chains; Traffic models
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math8030333

Drone logistics can play an important role in logistics at the end of the supply chain and special environmental logistics. At present, drone logistics is in the initial development stage, and the location of drone logistics hubs is an important issue in the optimization of logistics systems. This paper implements a compact cuckoo search algorithm with mixed uniform sampling technology, and, for the problem of weak search ability of the algorithm, this paper combines the method of recording the key positions of the search process and increasing the number of generated solutions to achieve further improvements, as well as implements the improved compact cuckoo search algorithm. Then, this paper uses 28 test functions to verify the algorithm. Aiming at the problem of the location of drone logistics hubs in remote areas or rural areas, this paper establishes a simple model that considers the traffic around the village, the size of the village, and other factors. It is suitable for selecting the location of the logistics hub in advance, reducing the cost of drone logistics, and accelerating the large-scale application of drone logistics. This paper uses the proposed algorithm for testing, and the test results indicate that the proposed algorithm has strong competitiveness in the proposed model.