technical-note

Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid

Authors:

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2263 - 2268

https://doi.org/10.1145/1570256.1570315

Published: 08 July 2009 Publication History

Get Access

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid

Pages 2263 - 2268

Abstract
References

Abstract

This paper benchmarks an Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimizer (PSO) on noise-free BBOB 2009 testbed. The algorithm is referred to as EDA-PSO and further enhanced with correlation-triggered adaptive variance scaling.

References

[1]

R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In Proc. of the 6th International Symposium on Micro Machine and Human Science, pages 39--43, 1995.

Crossref

Google Scholar

[2]

M. El-Abd and M. S. Kamel. Preventing premature convergence in a pso and eda hybrid. In Accepted IEEE Congress on Evolutionary Computation, 2009.

Digital Library

Google Scholar

[3]

S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE, 2009.

Google Scholar

[4]

J. Grahl, P. A. N. Bosman, and F. Rothlauf. The correlation-triggered adaptive variance scaling idea. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 397--404, 2006.

Digital Library

Google Scholar

[5]

N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black--box optimization benchmarking 2009: Experimental setup. Technical Report RR--6828, INRIA, 2009.

Google Scholar

[6]

N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions. Technical Report RR-6829, INRIA, 2009.

Google Scholar

[7]

J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proc. of IEEE International Conference on Neural Networks, volume 4, pages 1942--1948, 1995.

Crossref

Google Scholar

[8]

Y. Zhou and J. Jin. Eda-pso -- a new hybrid intelligent optimization algorithm. In Proc. of the Michigan University Graduate Student Symposium, 2006.

Google Scholar

Cited By

View all

Singh PKottath RTejani G(2022)Ameliorated Follow The Leader: Algorithm and Application to Truss Design ProblemStructures10.1016/j.istruc.2022.05.10542(181-204)Online publication date: Aug-2022
https://doi.org/10.1016/j.istruc.2022.05.105
García-Valdez MMerelo J(2021)Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary StrategiesApplications of Evolutionary Computation10.1007/978-3-030-72699-7_47(747-762)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_47
Zhang LSrisukkham WNeoh SLim CPandit D(2018)Classifier ensemble reduction using a modified firefly algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.10.00193:C(395-422)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.eswa.2017.10.001
Show More Cited By

Index Terms

Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms

Recommendations

Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization
GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

This paper benchmarks the Particle Swarm Optimization (PSO) algorithm using the noise-free BBOB 2009 testbed.
Black-box optimization benchmarking for noiseless function testbed using PSO_bounds
GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

This paper benchmarks the particle swarm optimizer with adaptive bounds algorithm (PSO Bounds) on the noisefree BBOB 2009 testbed. The algorithm is further augmented with a simple re-initialization mechanism that is invoked if the bounds tend to ...
Noiseless functions black-box optimization: evaluation of a hybrid particle swarm with differential operators
GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

In this work we evaluate a Particle Swarm Optimizer hybridized with Differential Evolution and apply it to the Black-Box Optimization Benchmarking for noiseless functions (BBOB 2009). We have performed the complete procedure established in this special ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

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: 08 July 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Technical-note

Conference

GECCO09

Sponsor:

GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
152
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Singh PKottath RTejani G(2022)Ameliorated Follow The Leader: Algorithm and Application to Truss Design ProblemStructures10.1016/j.istruc.2022.05.10542(181-204)Online publication date: Aug-2022
https://doi.org/10.1016/j.istruc.2022.05.105
García-Valdez MMerelo J(2021)Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary StrategiesApplications of Evolutionary Computation10.1007/978-3-030-72699-7_47(747-762)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_47
Zhang LSrisukkham WNeoh SLim CPandit D(2018)Classifier ensemble reduction using a modified firefly algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.10.00193:C(395-422)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.eswa.2017.10.001
Mishra KTripathi ATiwari SSaxena N(2017)Evolution based memetic algorithm and its application in software cost estimationJournal of Intelligent & Fuzzy Systems10.3233/JIFS-1646332:3(2485-2498)Online publication date: 24-Feb-2017
https://doi.org/10.3233/JIFS-16463
(2017)A nature inspired hybrid optimisation algorithm for dynamic environment with real parameter encodingInternational Journal of Bio-Inspired Computation10.1504/IJBIC.2017.08533310:1(24-32)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1504/IJBIC.2017.085333
Strasser SGoodman RSheppard JButcher SNeumann FSutton A(2016)A New Discrete Particle Swarm Optimization AlgorithmProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908935(53-60)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908935
Bolufe Rohler AChen S(2012)Multi-swarm hybrid for multi-modal optimization2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256566(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256566
Hansen NAuger ARos RFinck SPošík PPelikan MBranke J(2010)Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009Proceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830790(1689-1696)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830790

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

Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization

Black-box optimization benchmarking for noiseless function testbed using PSO_bounds

Noiseless functions black-box optimization: evaluation of a hybrid particle swarm with differential operators

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