Article ID Journal Published Year Pages File Type
4942759 Engineering Applications of Artificial Intelligence 2017 11 Pages PDF
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 320 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
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