Artificial Intelligence
Three Scheduling Algorithms Applied to the Earth Observing Systems Domain
Management Science
Daily imaging scheduling of an Earth observation satellite
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
A multi-objective chance constrained programming model(MOCCPM) for electronic reconnaissance satellites scheduling problem(ERSSP) is presented. MOCCPM takes the uncertainties in the course of satellite electronic reconnaissance into account, as well as the capabilities and usage restrictions of the electronic reconnaissance satellites. Then a Monte Carlo simulation based multi-objective extremal optimization (MCSBMOEO) algorithm is proposed. Penalty function based fitness assignment ensures the efficient evolution. Problem specific mutation operator ensures the feasibility of the offspring so as to prevent the algorithm from falling into local optimum. External archive is to keep the nondominated solutions and guarantee their diversity. Monte Carlo sampling is to address the stochastic nature of ERSSP. The experiment results testified that the algorithm can solve ERSSP effectively.