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.

We propose a platform-independent multithread routing method for FPGAs including two aspects: single high fanout net is routed parallel within itself and several low fanout nets are routed parallel between themselves. Routing for high fanout nets usually takes considerable time because of the large physical area surrounded by bounding boxes to traverse and tens of terminals to connect. Therefore, one high fanout net is partitioned into several subnets with fewer terminals and smaller bounding boxes to be routed in parallel. However, low fanout nets with intrinsic small bounding boxes and few terminals could hardly be divided. Instead, low fanout nets whose bounding boxes are not overlapping with each other are routed concurrently. A new graph, named bounding box graph, was utilized to facilitate the process of selecting several nets to be routed concurrently. In this graph, one vertex stands for a corresponding net and one edge between two connected vertex means that the two represented nets have their bounding boxes overlapped. Several strategies are introduced to balance the load among threads and ensure the deterministic results. The routing times scale down with increasing number of threads. On a 4-core processor, this technique improves the run-time by ~1.9 × with routing quality degrading by no more than 2.3%.