MOEA/D vs. NSGA-II: A Comprehensive Comparison for Multi/Many Objective Analog/RF Circuit Optimization through a Generic Benchmark

Abstract

Thanks to the enhanced computational capacity of modern computers, even sophisticated analog/radio frequency (RF) circuit sizing problems can be solved via electronic design automation (EDA) tools. Recently, several analog/RF circuit optimization algorithms have been successfully applied to automatize the analog/RF circuit design process. Conventionally, metaheuristic algorithms are widely used in optimization process. Among various nature-inspired algorithms, evolutionary algorithms (EAs) have been more preferred due to their superiorities (robustness, efficiency, accuracy etc.) over the other algorithms. Furthermore, EAs have been diversified and several distinguished analog/RF circuit optimization approaches for single-, multi-, and many-objective problems have been reported in the literature. However, there are conflicting claims on the performance of these algorithms and no objective performance comparison has been revealed yet. In the previous work, only a few case study circuits have been under test to demonstrate the superiority of the utilized algorithm, so a limited comparison has been made for only these specific circuits. The underlying reason is that the literature lacks a generic benchmark for analog/RF circuit sizing problem. To address these issues, we propose a comprehensive comparison of the most popular two evolutionary computation algorithms, namely Non-Sorting Genetic Algorithm-II and Multi-Objective Evolutionary Algorithm based Decomposition, in this article. For that purpose, we introduce two ad hoc testbenches for analog and RF circuits including the common building blocks. The comparison has been made at both multi- and many-objective domains and the performances of algorithms have been quantitatively revealed through the well-known Pareto-optimal front quality metrics.