Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems

Karam M. Sallam; Saber M. Elsayed; Ruhul A. Sarker; Daryl L. Essam

doi:10.1007/978-3-319-49049-6_27

Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems

Karam M. Sallam, Saber M. Elsayed, Ruhul A. Sarker, Daryl L. Essam

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem’s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.

Original language	English
Title of host publication	Intelligent and Evolutionary Systems
Subtitle of host publication	The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings
Editors	George Leu, Hemant Kumar Singh, Saber Elsayed
Place of Publication	Netherlands
Publisher	Springer
Chapter	27
Pages	371-387
Number of pages	18
ISBN (Electronic)	9783319490496
ISBN (Print)	9783319490489
DOIs	https://doi.org/10.1007/978-3-319-49049-6_27
Publication status	Published - 9 Nov 2016
Externally published	Yes
Event	20th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2016) - Canberra, Canberra, Australia Duration: 1 Nov 2016 → 1 Nov 2016

Publication series

Name	Intelligent and Evolutionary Systems
Volume	8
ISSN (Print)	2363-6084
ISSN (Electronic)	2363-6092

Conference

Conference	20th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2016)
Abbreviated title	IES 2016
Country/Territory	Australia
City	Canberra
Period	1/11/16 → 1/11/16

Access to Document

10.1007/978-3-319-49049-6_27

http://link.springer.com/10.1007/978-3-319-49049-6_27

Cite this

Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2016). Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. In G. Leu, H. K. Singh, & S. Elsayed (Eds.), Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings (pp. 371-387). (Intelligent and Evolutionary Systems; Vol. 8). Springer. https://doi.org/10.1007/978-3-319-49049-6_27

Sallam, Karam M. ; Elsayed, Saber M. ; Sarker, Ruhul A. et al. / Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings. editor / George Leu ; Hemant Kumar Singh ; Saber Elsayed. Netherlands : Springer, 2016. pp. 371-387 (Intelligent and Evolutionary Systems).

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title = "Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems",

abstract = "In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem{\textquoteright}s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.",

author = "Sallam, {Karam M.} and Elsayed, {Saber M.} and Sarker, {Ruhul A.} and Essam, {Daryl L.}",

year = "2016",

month = nov,

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doi = "10.1007/978-3-319-49049-6_27",

language = "English",

isbn = "9783319490489",

series = "Intelligent and Evolutionary Systems",

publisher = "Springer",

pages = "371--387",

editor = "George Leu and Singh, {Hemant Kumar} and Saber Elsayed",

booktitle = "Intelligent and Evolutionary Systems",

address = "Netherlands",

note = "20th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2016), IES 2016 ; Conference date: 01-11-2016 Through 01-11-2016",

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Sallam, KM, Elsayed, SM, Sarker, RA & Essam, DL 2016, Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. in G Leu, HK Singh & S Elsayed (eds), Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings. Intelligent and Evolutionary Systems, vol. 8, Springer, Netherlands, pp. 371-387, 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2016), Canberra, Australian Capital Territory, Australia, 1/11/16. https://doi.org/10.1007/978-3-319-49049-6_27

Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. / Sallam, Karam M.; Elsayed, Saber M.; Sarker, Ruhul A. et al.
Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings. ed. / George Leu; Hemant Kumar Singh; Saber Elsayed. Netherlands: Springer, 2016. p. 371-387 (Intelligent and Evolutionary Systems; Vol. 8).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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AU - Sallam, Karam M.

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AU - Essam, Daryl L.

PY - 2016/11/9

Y1 - 2016/11/9

N2 - In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem’s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.

AB - In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem’s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.

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Sallam KM, Elsayed SM, Sarker RA, Essam DL. Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. In Leu G, Singh HK, Elsayed S, editors, Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings. Netherlands: Springer. 2016. p. 371-387. (Intelligent and Evolutionary Systems). doi: 10.1007/978-3-319-49049-6_27

Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems

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