The Improvement of the Honey Badger Algorithm and Its Application in the Location Problem of Logistics Centers

Abstract

Aiming at the problems of low resource utilization and high distribution cost of urban logistics enterprises, this paper introduces the threshold setting of large parcels, comprehensively considers the processing links of large parcels and standard parcels in loading, unloading, sorting, and other processing links, and constructs a logistics planning model with the type of multi-functional transit center as the variable and the total cost of the logistics system as the goal. Aiming at the shortcomings of the honey badger algorithm, three optimization strategies are used to improve the logistics model, and the effectiveness of the improved algorithm is verified by comparing with the CPLEX operation results. Based on the operation data of SF Jinzhou, this paper obtains the optimization results of large parcel threshold, multi-function transit center location layout, and terminal demand point allocation. From the results, the introduction of the threshold setting for large parcels has played a significant role in the joint optimization of multi-functional center location selection and terminal demand point allocation under multi-parcel distribution and provides theoretical data support for the existing urban logistics location problem.

Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment

“…HBA is a newlyestablished metaheuristic approach inspired by the smart foraging behavior of honey badgers (HB). In this approach, the honey-finding and digging approaches of HB are mimicked during the exploitation and exploration stages in the search range [22,23]. The initial population randomly initializes the individual location and the size of the population (number of HBs), as given below:…”