Multiobjective analog/RF circuit sizing using an improved brain storm optimization algorithm

Abstract

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.

Appendix

To demonstrate the applicability of proposed IMBSO algorithm in solving other constrained multiobjective problems, we choose four standard test problems [9], i.e., BNH, TNK, OSY and CEX. Pareto fronts of all four constrained test problems are generated and shown in Fig. 9. The population size is set to 100 and the maximum generation is kept at 1000 for efficient evaluation. Different parameters of IMBSO algorithm are set as described in Sect. 5.1. The BNH and CEX problems are simple as compared to TNK and OSY test problems in that the constraints may not introduce additional strain in finding optimal Pareto fronts. However, constraints in TNK and OSY test problems make the Pareto front discontinuous. The discontinuity in Pareto fronts demand to maintain nondominated solutions at different intersections of constraint boundaries. It can be observed from Fig. 9 that the proposed IMBSO algorithm displays smooth distributions of nondominated solutions along the true Pareto fronts of four test problems.