An Evolutionary Algorithm With Memory Guidance for Data Transmission Scheduling Optimization in Communication Satellite Network

Yingying Ren, Qiuli Li, Yue Zhang, Jiting Li, Witold Pedrycz, Ponnuthurai Nagaratnam Suganthan, Rammohan Mallipeddi, Yanjie Song

Abstract: With the rapid development of satellite technology, communication satellites have become an indispensable part of modern infrastructure. They serve as a key pillar for the future integrated communication satellite network (CSN). However, the increasing number of communication satellites presents significant challenges for data transmission between the satellite and ground station. This article focuses on transmitting communication data by scheduling resources for communication tasks. The goal of data transmission scheduling optimization in CSN (DTSOCSN) is to design a scheduling scheme that maximizes task profit across satellite–ground links, considering the constraints of the two working modes of communication satellites. To solve DTSOCSN, a mixed-integer programming model is developed, which incorporates various constraints such as the conditions for feed switching operation and the limitations of task execution windows. Based on the complexity of the problem, we propose an evolutionary algorithm with memory guidance (MGEA). The algorithm takes into account the memory dependence of Caputo fractional-order differential and innovatively designs a crossover operator, called Caputo crossover (CX). This crossover method uses the genetic information stored in memory to guide the crossover operation of the next generation, thereby forming a smooth optimization path and improving the search efficiency of the algorithm. In addition, an elite opposition-based heuristic initialization method and a tracking variation strategy are designed to enhance the algorithm’s ability to find high-quality initial solutions and perform local optimization. Experimental validation proceeds in two stages: first, multiscale simulations demonstrate MGEA’s superior performance over existing mainstream algorithms in task profit, convergence speed, resource utilization, and search efficiency. Second, to verify the contribution of the CX operator, it is integrated into several classical algorithms for comparative testing on benchmark problems. The results consistently show that algorithms using the CX operator achieve significant performance advantages compared with those relying on traditional crossover operators. This study not only provides an effective solution for DTSOCSN but also offers new idea for solving other types of satellite scheduling problems.

External IDs:dblp:journals/tsmc/RenLZLPSMS26