Title
A memetic approach to the automatic design of high-performance analog integrated circuits
Abstract
This article introduces an evolution-based methodology, named memetic single-objective evolutionary algorithm (MSOEA), for automated sizing of high-performance analog integrated circuits. Memetic algorithms may achieve higher global and local search ability by properly combining operators from different standard evolutionary algorithms. By integrating operators from the differential evolution algorithm, from the real-coded genetic algorithm, operators inspired by the simulated annealing algorithm, and a set of constraint handling techniques, MSOEA specializes in handling analog circuit design problems with numerous and tight design constraints. The method has been tested through the sizing of several analog circuits. The results show that design specifications are met and objective functions are highly optimized. Comparisons with available methods like genetic algorithm and differential evolution in conjunction with static penalty functions, as well as with intelligent selection-based differential evolution, are also carried out, showing that the proposed algorithm has important advantages in terms of constraint handling ability and optimization quality.
Year
DOI
Venue
2009
10.1145/1529255.1529264
ACM Trans. Design Autom. Electr. Syst.
Keywords
Field
DocType
analog circuit sizing,genetic algorithm,simulated annealing algorithm,analog circuit design problem,automatic design,real-coded genetic algorithm,memetic single-objective evolutionary algorithm,proposed algorithm,integrated circuit,high-performance analog,constrained optimization,analog design automation,different standard evolutionary algorithm,memetic algorithm,analog circuit,memetic approach,differential evolution algorithm,analog circuits,local search,evolutionary algorithm,design automation,objective function,penalty function,differential evolution
Simulated annealing,Memetic algorithm,Mathematical optimization,Analogue electronics,Evolutionary algorithm,Computer science,Differential evolution,Local search (optimization),Genetic algorithm,Constrained optimization
Journal
Volume
Issue
ISSN
14
3
1084-4309
Citations 
PageRank 
References 
6
0.57
16
Authors
5
Name
Order
Citations
PageRank
Bo Liu1384.72
Francisco V. Fernández223440.82
Georges G. E. Gielen32036254.40
R. Castro-López47918.20
Elisenda Roca512926.84