A diverse/converged individual competition algorithm for computationally expensive many-objective optimization

Surrogate-assisted evolutionary algorithms (SAEAs) are popular for solving expensive optimization problems. However, most existing SAEAs are designed for solving single-objective or multiobjective optimization problems with two or three objectives. Few works had been reported to deal with expensive many-objective optimization problems with more than three objectives because of two difficulties. One is the curse of dimensionality caused by many-objective problems, and the other is the fewer computational resources available in a limited time for expensive optimization problems. Since an effective selection method can better solve the many-objective optimization problems, high-efficiency search and accurate model can save computational resources for expensive optimization problems, this paper proposes a diverse/converged individual competition algorithm, which owns a novel diverse/converged individual competition selection mechanism, a hybrid search mechanism, and a segmentation approach. The diverse/converged individual competition selection mechanism maintains a good balance between the convergence and diversity of the selected solutions for solving many-objective optimization problems. The hybrid search mechanism performs a memetic search and genetic search at different stages of the evolution process to further generate superior solutions. The segmentation approach uses two different populations with small numbers to build two surrogate models which will predict different areas, and it can improve the accuracy of the prediction. The proposed algorithm is compared with several state-of-art algorithms on widely used benchmark functions. The experimental results show that the proposed algorithm performs significantly better than the compared algorithms.