KIDEA: A Novel Multi-Objective Optimization Algorithm and its Application in Analog Circuit Design

Conventional NSGA-II encounters significant difficulties in automatic tuning of design parameters for analog circuit design. In response, We present KIDEA, an enhancement of NSGA-II through differential evolution, Isolation Forest, and KMeans clustering. Differential evolution improves efficiency and convergence, while Isolation Forest and KMeans jointly elevate the quality of pareto optimal solutions by refining outlier detection and solution clustering. The algorithm is used to tune the design parameters in analog circuit design. Compared to conventional NSGA-II, the results from KIDEA show average dominance improvement of 34.0% and convergence rate improvement of 3.14 times.