Mixing global and local competition in genetic optimization based design space exploration of analog circuits

Abstract

The knowledge of optimal design space boundaries of component circuits can be extremely useful in making good subsystem-level design decisions which are aware of the parasitics and other second-order circuit-level details. However, direct application of popular Multi-objective genetic optimization algorithms were found to produce Pareto fronts with poor diversity for analog circuits problems. This work proposes a novel approach to control the diversity of solutions by paritioning the solution space, using Local Competition to promote diversity and Global competition for convergence, and by controlling the proportion of these two mechanisms by a Simulated Annealing based formulation. The algorithm was applied to extract numerical results on analog switched capacitor integrator circuits with a wide range of tight specifications. The results were found to be significantly better than traditional GA based uncontrolled optimization methods.