Multi-satellite control resource scheduling based on ant colony optimization

作者：

Highlights：

• Pheromone trail updates by two stages to avoid algorithm trapping in local optima.

• The global exploration ability and solution quality of the MSCRSA–ACO is superior to existed algorithms, such as GA, IR and MMAS.

• Complex independent set model (CISM) is developed based on visible arcs and working periods.

摘要

•Pheromone trail updates by two stages to avoid algorithm trapping in local optima.•The global exploration ability and solution quality of the MSCRSA–ACO is superior to existed algorithms, such as GA, IR and MMAS.•Complex independent set model (CISM) is developed based on visible arcs and working periods.

论文关键词：Ant colony optimization,Visible arc,Complex independent set model,Two stages

论文评审过程：Available online 14 October 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.10.014