poster

Alpinist CellularDE: a cellular based optimization algorithm for dynamic environments

Authors:

Vahid Noroozi,

Ali B. Hashemi,

Mohammad Reza MeybodiAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 1519 - 1520

https://doi.org/10.1145/2330784.2331024

Published: 07 July 2012 Publication History

Get Access

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Alpinist CellularDE: a cellular based optimization algorithm for dynamic environments

Pages 1519 - 1520

Abstract
References

Abstract

In this paper, we propose Alpinist CellularDE to address dynamic optimization problems. Alpinist CellularDE tries to detect different regions of the landscape and uses this information to perform more effective search and increase its performance. Moreover, in Alpinist CellularDE a directed local search is proposed to track local optima after detecting a change in the environment. The proposed algorithm is evaluated on various dynamic environments, modeled by Moving Peaks Benchmark. Experiments show superior performance of Alpinist CellularDE in all test cases in comparison with some of the best performing evolutionary algorithms for dynamic optimization problems.

References

[1]

Cruz, C., González, J. R., and Pelta, D. A. Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing-A Fusion of Foundations, Methodologies and Applications, (2011), 1--22.

Digital Library

Google Scholar

[2]

Noroozi, V., Hashemi, A., and Meybodi, CellularDE: a cellular based differential evolution for dynamic optimization problems. In Proceedings of the Adaptive and Natural Computing Algorithms. 340--349.

Digital Library

Google Scholar

[3]

Hashemi, A. and Meybodi, M. 2009. Cellular PSO: A PSO for Dynamic Environments. In Proceedings of the Advances in Computation and Intelligence. 422--433.

Digital Library

Google Scholar

[4]

Hashemi, A. and Meybodi, M. 2009. A multi-role cellular PSO for dynamic environments. In Proceedings of the 14th International CSI Computer Conference. 412--417.

Google Scholar

[5]

Branke, J. 2002. Memory enhanced evolutionary algorithms for changing optimization problems. In Proceedings of the IEEE Congress on Evolutionary Computation,1875--1882.

Google Scholar

[6]

Blackwell, T., Branke, J., and Li, X. Particle swarms for dynamic optimization problems. Swarm Intelligence, (2008), 193--217.

Crossref

Google Scholar

[7]

Hooke, R. and Jeeves, T. A. "Direct Search" Solution of Numerical and Statistical Problems. Journal of the ACM, 8 (1961), 212--229.

Digital Library

Google Scholar

Cited By

View all

Rezvanian AVahidipour SSaghiri A(2023)CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic EnvironmentsAlgorithms10.3390/a1701001817:1(18)Online publication date: 30-Dec-2023
https://doi.org/10.3390/a17010018
Kazemi Kordestani JRazapoor Mirsaleh MRezvanian AMeybodi M(2021)Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization ProblemsAdvances in Learning Automata and Intelligent Optimization10.1007/978-3-030-76291-9_8(287-321)Online publication date: 24-Jun-2021
https://doi.org/10.1007/978-3-030-76291-9_8
Kazemi Kordestani JRazapoor Mirsaleh MRezvanian AMeybodi M(2021)An Overview of Multi-population Methods for Dynamic EnvironmentsAdvances in Learning Automata and Intelligent Optimization10.1007/978-3-030-76291-9_7(253-286)Online publication date: 24-Jun-2021
https://doi.org/10.1007/978-3-030-76291-9_7
Show More Cited By

Recommendations

CellularDE: a cellular based differential evolution for dynamic optimization problems
ICANNGA'11: Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I

In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an ...
Variations of biogeography-based optimization and Markov analysis

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and ...
A differential evolution algorithm with intersect mutation operator

This paper proposes a novel differential evolution (DE) algorithm with intersect mutation operation called intersect mutation differential evolution (IMDE) algorithm. Instead of focusing on setting proper parameters, in IMDE algorithm, all individuals ...

Comments

Information & Contributors

Information

Published In

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Poster

Conference

GECCO '12

Sponsor:

SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
58
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

Rezvanian AVahidipour SSaghiri A(2023)CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic EnvironmentsAlgorithms10.3390/a1701001817:1(18)Online publication date: 30-Dec-2023
https://doi.org/10.3390/a17010018
Kazemi Kordestani JRazapoor Mirsaleh MRezvanian AMeybodi M(2021)Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization ProblemsAdvances in Learning Automata and Intelligent Optimization10.1007/978-3-030-76291-9_8(287-321)Online publication date: 24-Jun-2021
https://doi.org/10.1007/978-3-030-76291-9_8
Kazemi Kordestani JRazapoor Mirsaleh MRezvanian AMeybodi M(2021)An Overview of Multi-population Methods for Dynamic EnvironmentsAdvances in Learning Automata and Intelligent Optimization10.1007/978-3-030-76291-9_7(253-286)Online publication date: 24-Jun-2021
https://doi.org/10.1007/978-3-030-76291-9_7
Kazemi Kordestani JMeybodi MRahmani A(2020)A Two-Level Function Evaluation Management Model for Multi-Population Methods in Dynamic Environments: Hierarchical Learning Automata ApproachJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2020.172156833:1(1-26)Online publication date: 5-Feb-2020
https://doi.org/10.1080/0952813X.2020.1721568
Kordestani JMeybodi M(2020)Application of Sub‐Population Scheduling Algorithm in Multi‐Population Evolutionary Dynamic OptimizationEvolutionary Computation in Scheduling10.1002/9781119574293.ch7(169-211)Online publication date: 12-May-2020
https://doi.org/10.1002/9781119574293.ch7
Kordestani JFirouzjaee HReza Meybodi M(2018)An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problemsApplied Intelligence10.1007/s10489-017-0963-748:1(97-117)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10489-017-0963-7
Kordestani JAhmadi AMeybodi M(2018)An improved Differential Evolution algorithm using learning automata and population topologiesApplied Intelligence10.1007/s10489-014-0585-241:4(1150-1169)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-014-0585-2
Kordestani JRezvanian AMeybodi M(2018)CDEPSOApplied Intelligence10.1007/s10489-013-0483-z40:4(682-694)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-013-0483-z
Bahaadini SRohani NCoughlin SZevin MKalogera VKatsaggelos A(2017)Deep multi-view models for glitch classification2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2017.7952693(2931-2935)Online publication date: Mar-2017
https://doi.org/10.1109/ICASSP.2017.7952693
Kordestani JRezvanian AMeybodi M(2017)New measures for comparing optimization algorithms on dynamic optimization problemsNatural Computing10.1007/s11047-016-9596-8Online publication date: 3-Jan-2017
https://doi.org/10.1007/s11047-016-9596-8

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

Recommendations

CellularDE: a cellular based differential evolution for dynamic optimization problems

Variations of biogeography-based optimization and Markov analysis

A differential evolution algorithm with intersect mutation operator

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

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