MOEA based memetic algorithms for multi-objective satellite range scheduling problem

Satellite range scheduling plays a very important role in guaranteeing the normal operation and the real-time control of in-orbit satellites. Although there appears a stronger demand for multi-objective optimization of satellite monitoring departments, multiple scheduling criteria are rarely considered simultaneously. To address the multi-objective satellite range scheduling problem (MOSRSP), a general MOEA based memetic algorithm (MOEAMA) framework is proposed, which optimizes the failure rate of ground-satellite communication requests and the load-balance degree of remote-tracking antennas. Based on a novel decision model for MOSRSP, the conflict-resolution and load-balance operators and the tabu search metaheuristic are designed to implement the local search operations in the MOEA-MA. Different types of the MOEAs, including the domination-based MOEAs, decomposition-based MOEAs and metric-based MOEAs are adopted to implement the evolutionary operations in the MOEA-MA. The highlight of this paper is the effective application of the MOEA-MAs to practical scheduling problems, where the two most concerning objectives are well addressed. The MOEA-MAs that adopt five wellknown MOEAs are given and examined by the Benchmarks problems. Computational results indicate that the MOEA-MM outperform the original MOEAs in terms of the metrics of coverage, hypervolume and spacing, which show good performance and application prospect for the MOSRSP.