Optimally selecting a subset of targets from a larger catalog is a common problem in astronomy and cosmology. A specific example is the selection of targets from an imaging survey for multi-object spectrographic follow-up. We present a new heuristic algorithm, HYBRID, for this purpose and undertake detailed studies of its performance. HYBRID combines elements of the simulated annealing, MCMC and particle-swarm methods and is particularly successful in cases where the survey landscape has multiple curvature or clustering scales. HYBRID consistently outperforms the other methods, especially in high-dimensionality spaces with many extrema. This means many fewer simulations must be run to reach a given performance confidence level and implies very significant advantages in solving complex or computationally expensive optimisation problems.
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