Multi-Objective Evolutionary Algorithms and Pattern Search Methods for Circuit Design Problems

Tonio Biondi (STMicroeletronics, Italy)

Angelo Ciccazzo (STMicroeletronics, Italy)

Vincenzo Cutello (University of Catania, Italy)

Santo D'Antona (University of Catania, Italy)

Giuseppe Nicosia (University of Catania, Italy)

Salvatore Spinella (University of Calabria, Italy)

Abstract: The paper concerns the design of evolutionary algorithms and pattern search methods on two circuit design problems: the multi-objective optimization of an Operational Transconductance Amplifier and of a fifth-order leapfrog filter. The experimental results obtained show that evolutionary algorithms are more robust and effective in terms of the quality of the solutions and computational effort than classical methods. In particular, the observed Pareto fronts determined by evolutionary algorithms has a better spread of solutions with a larger number of nondominated solutions when compared to the classical multi-objective techniques.

Keywords: circuit design problems, classical optimization methods, evolutionary algorithms, evolutionary electronics, genetic algorithms, leapfrog filter, multi-objective optimization, operational transconductance amplifier, pattern search methods

Categories: B.7.1, B.7.2, I.2.8, J.2