Expected Improvement Matrix-Based Infill Criteria for Expensive Multiobjective Optimization

摘要

The existing multiobjective expected improvement (EI) criteria are often computationally expensive because they are calculated using multivariate piecewise integrations, the number of which increases exponentially with the number of objectives. In order to solve this problem, this paper proposes a new approach to develop cheap-to-evaluate multiobjective EI criteria based on the proposed EI matrix (EIM). The elements in the EIM are the single-objective EIs that the studying point has beyond each Pareto front approximation point in each objective. Three multiobjective criteria are developed by combining the elements in the EIM into scalar functions in three different ways. These proposed multiobjective criteria are calculated using only 1-D integrations, whose number increases linearly with respect to the number of objectives. Moreover, all the three criteria are derived in closed form expressions, thus are significantly cheaper to evaluate than the state-of-the-art multiobjective criteria. The efficiencies of the proposed criteria are validated through 12 test problems. Besides the computational advantage, the proposed multiobjective EI criteria also show competitive abilities in approximating the Pareto fronts of the chosen test problems compared against the state-of-the-art multiobjective EI criteria.