Scheduling earth observing fleets using evolutionary algorithms: Problem description and approach

We hypothesize that evolutionary algorithms can effec-tively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can repre-sent solutions, modify solutions, and evaluate solution fit-ness. To test the hypothesis we have developed a represen-tative set of problems, optimization software (in Java) to solve them, and run experiments. This paper presents ini-tial results including a comparison of separate vs. inte-grated scheduling of a two satellite constellation and a comparison of several evolutionary and other optimiza-tion techniques; namely genetic algorithms, simulated an-nealing, squeaky wheel optimization, and stochastic hill climbing.