An Improved Knowledge-Based Genetic Algorithm for High-Priority Task Scheduling in Relay Satellite Networks
Source abstract
Abstract
This paper presents an improved knowledge-based genetic algorithm for relay satellite task scheduling. It aims to address the issue of ensuring high-priority tasks under complex constraints and resource competition. The algorithm enhances the high-priority task completion ratio by incorporating multiple knowledge sorting strategies and a dynamic crossover mechanism during population initialization. Simulation results show that the proposed algorithm improves the high-priority task completion ratio by an average of 11.78 percentage points compared to the second-best algorithm. In environments with high load, resource constraints, and capacity shrinkage, the proposed algorithm outperforms in scheduling efficiency, robustness, and adaptability. It demonstrates effectiveness in multi-task and multi-resource competitive environments. IKBGA provides a highly targeted and scalable optimization solution for relay satellite task scheduling, with strong application potential.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Satellite Communication Systems · Opportunistic and Delay-Tolerant Networks · IoT Networks and Protocols
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Funding
National Natural Science Foundation of China
References (33)
Show source-provided references
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