Multi-Objective Design Optimization of Planar Spiral Inductors Using Enhanced Metaheuristic Techniques

Abstract

The study presented in this paper improves the Multi-Objective Artificial Bee Colony (MOABC) method. It evaluates its performance using Generational Distance (GD), Spread (SP), and Hypervolume (HV) metrics on the Zitzler-Deb-Thiele (ZDT) benchmark functions. Subsequently, the improved MOABC method, along with Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is applied to optimize the design of a square planar spiral inductor. The objectives are to maximize the quality factor ($Q$) and minimize the inductor area ($A$) simultaneously while maintaining a necessary inductance of $4\, \text{nH}$ at a $2.4\, \text{GHz}$ operating frequency, utilizing $0.13\, \mu \text{m}$ CMOS technology. The optimization findings are verified and confirmed using Advanced Design System (ADS) Momentum, demonstrating the feasibility of multi-objective optimization for integrated inductor design.