Memetic algorithm using multiple surrogates for complex engineering design optimization

Abstract

Complex engineering design (CED) optimization problems in science and engineering commonly have large design spaces. In such design spaces, typically thousands of exact fitness evaluations are required to locate a near optimal design. Often in photonics, electromagnetic, aerospace, biomedical and microwave circuits detailed design processes, variable-fidelity analysis codes are employed to strike a balance between design cost, time and estimation accuracy. Nevertheless, in analysis and design optimization processes where high-fidelity analysis codes are used, each exact fitness evaluation requiring the simulation of analysis codes may cost hours of supercomputer time. Therefore, the overwhelming part of the total run time in CED optimization is usually taken up by the simulation of analysis codes. This often poses a serious impediment to the practical application of high-fidelity analysis codes and evolutionary algorithms to CED optimization problems. In this dissertation work, the research focus has been placed on the use of multiple surrogate models in standard memetic algorithm (MA) to mitigate the costly CED optimization process. In this thesis, a novel hierarchical surrogate-assisted memetic algorithm (HSAMA) combining both global and local surrogate models for accelerating the optimization process is proposed and described. The performance of the proposed algorithm is analyzed by using a series of commonly used benchmark test functions. Furthermore, the proposed algorithm is also applied to aerodynamic shape design. Numerical results show that the HSAMA algorithm is capable of achieving good designs efficiently under a limited computational budget.