Path Selection Optimization Algorithms for Mobile Agent Based on Push-All-Data Strategy

With the advent of 5G and 6G technologies and the growing ubiquity of the Internet, the Mobile Agent (MA) paradigm is increasingly seen as a promising alternative to the conventional client-server model. MAs, which are software entities capable of moving and processing data across different systems, offer potential efficiencies in data management. However, their operation in dynamic and mobile environments can lead to challenges, such as incomplete or delayed tasks. This study addresses these issues by focusing on reducing the relocation time of MAs. A numerical procedure and a streamlining strategy were developed to expedite the transfer of an agent from the source to the target hub. Utilizing the itinerary design pattern and the Ant Colony Optimization (ACO) algorithm, implemented via the Java Agent Development Framework (JADE), this study sought the most efficient path for the MA. The proposed algorithm demonstrated a significant improvement, selecting the optimal path in just 271.511 seconds. This performance represents a substantial enhancement over previous approaches using the master-slave design pattern with either the Genetic Algorithm (GA) or the Node Compression Algorithm (NCA). The implications of this improvement are far-reaching, potentially enhancing the efficiency and reliability of data management systems in a variety of applications.