Abstract Surrogate models are commonly used to reduce the number of required expensive fitness evaluations in optimizing computationally problems. Although many competitive surrogate-assisted evolutionary algorithms have been proposed, it remains a challenging issue develop an effective model management strategy address problems with different landscape features under limited computational budg...

Abstract Black-box optimization (BBO) algorithms are widely employed by practitioners to address computationally expensive real-world problems such as automatic tuning of machine learning models and evacuation route planning. The Nelder–Mead (NM) method is a well-known local search heuristic for BBO that has been applied solve many from way back because its promising performance. However, this ...

We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-regi...