Alternative Infill Strategies for Expensive Multi-Objective Optimisation
Association for Computing Machinery (ACM)
© 2017 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profi t or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci fic permission and/or a fee. Request permissions from firstname.lastname@example.org.
Reason for embargo
Embargoed until after conference
Many multi-objective optimisation problems incorporate computationally or financially expensive objective functions. State-of-the-art algorithms therefore construct surrogate model(s) of the parameter space to objective functions mapping to guide the choice of the next solution to expensively evaluate. Starting from an initial set of solutions, an infill criterion — a surrogate-based indicator of quality — is extremised to determine which solution to evaluate next, until the budget of expensive evaluations is exhausted. Many successful infill criteria are dependent on multi-dimensional integration, which may result in infill criteria that are themselves impractically expensive. We propose a computationally cheap infill criterion based on the minimum probability of improvement over the estimated Pareto set. We also present a range of set-based scalarisation methods modelling hypervolume contribution, dominance ratio and distance measures. These permit the use of straightforward expected improvement as a cheap infill criterion. We investigated the performance of these novel strategies on standard multi-objective test problems, and compared them with the popular SMS-EGO and ParEGO methods. Unsurprisingly, our experiments show that the best strategy is problem dependent, but in many cases a cheaper strategy is at least as good as more expensive alternatives.
This research was supported by the Engineering and Physical Sciences Research Council [grant number EP/M017915/1].
This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.
GECCO 2017: Genetic and Evolutionary Computation Conference, 15-19 July 2017, Berlin, Germany