A Constrained Sampling Assisted Differential Evolution for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have become a widely used method to solve computationally expensive optimization problems, in which the evaluation of objective has high time cost. However, in many problems, the evaluation of constraints also has high time cost. These problems are known as expensive constrained optimization problems (ECOPs). To solve ECOPs, this paper proposes a constrained sampling assisted differential evolution (CSADE) algorithm. CSADE consists a global search process and a local search process. Global search process aims to explore the whole search space. During this process, constraints are transformed as a penalty to fitness and a radial basis function (RBF) model is built for the penalty fitness. DE/rand/1 algorithm is adopted for candidate generation to improve the population diversity. Local search process aims to exploit the promising areas. In this process, the objective and each constraint are approximated by an RBF model respectively. DE/best/1 algorithm is utilized for population convergence in promising areas. Extensive experiments with several SAEAs for ECOPs on benchmark test suites demonstrate the competitive performance of CSADE. Besides, comparison experiments on the optimization of bulk carrier design show the promising application in engineering optimization of CSADE.