Procs of Workshop on Advances in Simulation-Driven Optimization and Modeling, ASDOM 2013, August 9-11, 2013, Reykjavik University, Iceland
Optimizing computationally expensive models we usually have to submit to strict limitations on amount of evaluations of models. These limitations become especially pressing in multi-objective optimization. A prominent approach to deal with such situations is surrogate-based optimization, where cheap synthetic models are used to approximate expensive models. In this talk we present a new surrogate-based multi-objective optimization algorithm based on a generalization of probability of improvement method.