This paper presents a multiobjective analog/RF circuit sizing tool using an improved brain storm optimization (IMBSO) algorithm with the purpose of analyzing the tradeoffs between competing performance specifications of analog/RF circuit block. A number of improvements are incorporated into IMBSO algorithm at different steps. At first, the clustering step of IMBSO algorithm is augmented with k-means\(++\) seeding technique to select the initial cluster centroids while clustering using k-means clustering technique. As a second improvement, the proposed IMBSO algorithm makes use of random probabilistic decision-making of river formation dynamics scheme to select optimal cluster centroids during population generation step. As a third improvement, an adaptive mutation operator is incorporated inside the IMBSO algorithm to generate new population. Finally, two separate constraint handling techniques are employed to handle both boundary and functional constraints during analog/RF circuit optimization. The performance of the proposed IMBSO algorithm is demonstrated in finding optimal Pareto fronts among different performance specifications of a two-stage operational amplifier circuit, a folded cascode amplifier circuit and a low noise amplifier circuit.

中文翻译：

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使用改进的头脑风暴优化算法确定多目标模拟/ RF电路的尺寸

本文介绍了一种使用改进的头脑风暴优化（IMBSO）算法的多目标模拟/ RF电路尺寸确定工具，目的是分析模拟/ RF电路模块的竞争性能规格之间的权衡。IMBSO算法在不同的步骤中进行了许多改进。起初，IMBSO算法的聚类步骤是增强与ķ -means \（++ \）播种技术来选择初始聚类质心，同时使用聚类ķ-均值聚类技术。作为第二个改进，提出的IMBSO算法利用河流形成动力学方案的随机概率决策来在种群生成步骤中选择最佳聚类质心。作为第三项改进，在IMBSO算法内部并入了一个自适应突变算子，以生成新种群。最后，在模拟/ RF电路优化期间，采用了两种单独的约束处理技术来处理边界约束和功能约束。通过在两级运算放大器电路，折叠共源共栅放大器电路和低噪声放大器电路的不同性能规格中找到最佳的Pareto前沿，证明了所提出的IMBSO算法的性能。