Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Experimental results on the application of satisfiability algorithms to scheduling problems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Algorithm performance and problem structure for flow-shop scheduling
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Earth Observation Satellite Management
Constraints
Three Scheduling Algorithms Applied to the Earth Observing Systems Domain
Management Science
Journal of Artificial Intelligence Research
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Combining genetic algorithms with squeaky-wheel optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Improving genetic algorithm performance with intelligent mappings from chromosomes to solutions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Simulation-based planning for planetary rover experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
A multi-objective imaging scheduling approach for earth observing satellites
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Graph colouring approaches for a satellite range scheduling problem
Journal of Scheduling
Operating system support for distributed applications in real space-time
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Understanding performance tradeoffs in algorithms for solving oversubscribed scheduling
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Understanding algorithm performance on an oversubscribed scheduling application
Journal of Artificial Intelligence Research
Generating optimised satellite payload operation schedules with evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Mixed discrete and continuous algorithms for scheduling airborne astronomy observations
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Computers and Operations Research
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Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks; conditions where stochastic algorithms are often effective. However, there are many such algorithms and the best one to use is not obvious. Here we compare multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized model EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple, greedy, deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best and random mutation outperforms a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call 'temperature-dependent swap' that makes large changes in the early stages of the search and smaller changes towards the end.