Multi-objective Hybrid Particle Swarm Optimization and its Application to Analog and RF Circuit Optimization

The presence of RF components in mixed-signal circuits make it a challenging task to resolve tradeoffs among performance specifications. In order to ease the process of circuit design, these tradeoffs are being analyzed using multi-objective optimization methodologies. This paper presents a hybrid multi-objective optimization framework (MHPSO), a combination of particle swarm optimization and simulated annealing. The framework emphasizes on preserving nondominated solutions in an external archive. The multi-dimensional space excluding the archive is divided into several sub-spaces according to a velocity-temperature mapping scheme. Further, the solutions in each sub-space are optimized using simulated annealing for generation of a Pareto front. The framework is extended by incorporating crowding distance comparison operator (MHPSO-CD) to maintain nondominated solutions in the archive. The effectiveness of proposed methodologies is demonstrated for performance space exploration of three electronic circuits, i.e., a two-stage operational amplifier, a folded cascode operational amplifier, and a low noise amplifier with inductive source degeneration. Additionally, the performance of proposed algorithms (MHPSO, MHPSO-CD) are evaluated on various test functions, and the results are compared with standard multi-objective evolutionary algorithms.