Surrogate-Assisted Multi-objective Differential Evolution based on Gaussian Process for Analog Circuit Synthesis

Inhalt:
In this paper, a surrogate-assisted multi-objective differential evolution based on Gaussian process is proposed for analog circuit synthesis. NSGA-II-DE is used as the multiobjective optimizer and online Gaussian process surrogate model is constructed to prescreen the best two trial vectors according to non-dominated sorting and modified crowding distance. Only two instead of multiple designs are simulated by HSPICE in one generation. The efficiency of proposed approach is verified on two real-world circuits. Compared with two state-of-the-art multiobjective evolutionary algorithms, our method can achieve better Pareto front (lowest I H) with much less number of simulations.