poster

Single-objective front optimization: application to rf circuit design

Authors:

E. J. Solteiro Pires,

Luís Mendes,

P. B. de Moura Oliveira,

J. A. Tenreiro Machado,

N. M. Fonseca Ferreira,

João Vaz,

Maria RosárioAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 765 - 766

https://doi.org/10.1145/1389095.1389245

Published: 12 July 2008 Publication History

Get Access

Abstract

This paper proposes a new algorithm which promotes well distributed non-dominated fronts in the parameters space when a single-objective function is optimized. This lgorithm is based on µ-dominance concept and maxmin sorting scheme. Besides that, the paper also presents the results of the algorithm when it is used in the automated synthesis of optimum performance CMOS radio-frequency and microwave binary-weighted differential switched capacitor arrays (RFDSCAs). The genetic synthesis tool optimizes a fitness function which is based on the performance parameter of the RFDSCAs. To validate the proposed design methodology, a CMOS RFDSCA is synthesized, using a 0.25 ¼m BiCMOS technology.

References

[1]

LAUMANNS, M. et. al Archiving with guaranteed convergence and diversity in multi-objective optimization. In Proc. of the GECCO (2002), pp. 439--447.

Digital Library

Google Scholar

[2]

PIRES, et. al Multi-Objective maximin sorting scheme. In EMO (2005), Springer-Verlag, LNCS Vol. 3410, pp. 165--175.

Digital Library

Google Scholar

[3]

MENDES, et. al A high-performance digitally controlled LC oscillator for ku-band applications. In IEEE Int. Conf. on Electronics, Circuits and Systems (Morroco, Dec. 2007).

Crossref

Google Scholar

[4]

DEB, K. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, LTD, 2001.

Digital Library

Google Scholar

Cited By

View all

Barbosa MPires ELopes A(2010)Optimization of Parallel Manipulators Using Evolutionary AlgorithmsSoft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010)10.1007/978-3-642-13161-5_11(79-86)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13161-5_11

Index Terms

Single-objective front optimization: application to rf circuit design
1. Computing methodologies

Recommendations

A Novel Hybrid Multi-objective Optimization Framework: Rotating the Objective Space
SEAL 2014: Proceedings of the 10th International Conference on Simulated Evolution and Learning - Volume 8886

Multi-objective Evolutionary Algorithms MOEAs are popular approaches for solving multi-objective problems MOPs. One representative method is Non-dominated Sorting Genetic Algorithm II NSGA-II, which has achieved great success in the field by introducing ...
A novel solver for multi-objective optimization: dynamic non-dominated sorting genetic algorithm (DNSGA)
Abstract
Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So, the complexity of non-dominated sorting method in a ...
A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization
Highlights
- An adaptive decomposition approach is proposed to guide the evolution process.
- ...
Abstract
In decomposition-based multi-objective evolutionary algorithms (MOEAs), a set of uniformly distributed reference vectors (RVs) is usually adopted to decompose a multi-objective optimization problem (MOP) into several single-objective ...

Comments

Information & Contributors

Information

Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
95
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Barbosa MPires ELopes A(2010)Optimization of Parallel Manipulators Using Evolutionary AlgorithmsSoft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010)10.1007/978-3-642-13161-5_11(79-86)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13161-5_11

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A Novel Hybrid Multi-objective Optimization Framework: Rotating the Objective Space

A novel solver for multi-objective optimization: dynamic non-dominated sorting genetic algorithm (DNSGA)

A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations