SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems

We present the algorithm SOCEMO for optimization problems that have multiple conflicting computationally expensive black-box objective functions. The computational expense arising from the objective function evaluations considerably restricts the number of evaluations that can be done to find Pareto-optimal solutions. Frequently used multiobjective optimization methods are based on evolutionary strategies and generally require a prohibitively large number of function evaluations to find a good approximation of the Pareto front. SOCEMO, in contrast, employs surrogate models to approximate the expensive objective functions. These surrogate models are used in the iterative sampling process to decide at which points in the variable domain the next expensive evaluations should be done. Therefore, fewer expensive objective function evaluations are needed, and a good approximation of the Pareto front can be found efficiently. Previous algorithms have generally been tested on problems with few variables (up to 10...