Scheduling earth observing satellites with evolutionary agorithms

We hypothesize that evolutionary algorithms can ef-fectively schedule coordinated fleets of Earth ob-serving satellites. The constraints are complex and the bottlenecks are not well understood, a condi-tion where evolutionary algorithms are often effec-tive. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a rep-resentative set of problems, produced optimization software (in Java) to solve them, and run experi-ments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the ge-netic algorithm [5], simulated annealing [7], squeaky wheel optimization [6], and stochastic hill climbing [1]. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.