Stochastic Ranking for Offline Data-Driven Evolutionary Optimization Using Radial Basis Function Networks with Multiple Kernels

摘要

For many real-world engineering optimization applications, evolutionary algorithms require a large number of fitness evaluations via expensive simulations or experiments. However, in some particular cases, no expensive fitness evaluations are available during the optimization process, which is called offline data-driven optimization. As the offline data is very limited, high-quality surrogate models must be built to take full advantage of the data. In this paper, a new stochastic ranking-based surrogate-assisted evolutionary algorithm is proposed to deal with offline data-driven optimization problems. To manage multiple models, stochastic ranking is employed. The experiment results on benchmark problems with up to 500 decision variables demonstrate that the proposed algorithm is effective on high dimensional problems.