Alternative infill strategies for expensive multi-objective optimisation

Alma A. M. Rahat, Richard M. Everson, Jonathan E. Fieldsend
2017 Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17  
Many multi-objective optimisation problems incorporate computationally or nancially expensive objective functions. State-of-theart 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 in ll criterion -a surrogate-based indicator of quality -is extremised to determine which solution to evaluate next, until the budget of expensive
more » ... tions is exhausted. Many successful in ll criteria are dependent on multi-dimensional integration, which may result in in ll criteria that are themselves impractically expensive. We propose a computationally cheap in ll 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. ese permit the use of straightforward expected improvement as a cheap in ll 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.
doi:10.1145/3071178.3071276 dblp:conf/gecco/RahatEF17 fatcat:4gliautez5eehcos6vkuybvtvu