Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms

Model-based multiobjective optimization is one class of metaheuris-tics for solving multiobjective optimization problems, where a probabilistic model is built from the current distribution of the solutions and new candidate solutions are generated from the model. One main difficulty in model-based optimization is constructing a probabilistic model that is able to effectively capture the structure of the problems to enable efficient search. This chapter advocates a new type of probabilistic model that takes the regularity in the distribution of Pareto-optimal solutions into account. We compare our model to two other model-based multiobjective algorithms on a number of test problems to demonstrate that it is scalable to high-dimensional optimization problems with or without linkage among the design variables.