Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942759 | Engineering Applications of Artificial Intelligence | 2017 | 11 Pages |
Abstract
Comparisons with a selection of state-of-the-art techniques (such as NSGA-II and YdIRCO) highlight the effectiveness of PareDA both in terms of Pareto optimality of the solutions found and time-to-converge. The solutions obtained by PareDA dominate those of comparative techniques, in particular, the proposed technique shows a significant average performance improvement (ranging from 35% to 49%) with respect to such techniques. Moreover, the CPU time required by PareDA to converge is smaller of at least 75% if compared with the other methodologies here analyzed (e.g. significantly improved designs for folded-cascode operational amplifier are found in just 320Â s). Finally, the PareDA algorithm can also benefit from parallelization, which leads to a significant speed-up with respect to the nonparallel version.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Andrea Patanè, Andrea Santoro, Piero Conca, Giovanni Carapezza, Antonino La Magna, Vittorio Romano, Giuseppe Nicosia,