Two-phase differential evolution framework for solving optimization problems

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

doi:10.1109/SSCI.2016.7850258

Two-phase differential evolution framework for solving 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

10 Citations (Scopus)

Abstract

Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design of an efficient selection mechanism to emphasize the best-performing DE variant has not yet been thoroughly explored. Motivated by this fact, in this paper, a new two-phase (exploration and exploitation) multi-operator DE algorithm is proposed. It starts with the exploration phase, dynamically placing emphasis on the best-performing DE based on two landscape indicators and its performance history, and then repeats this process during the exploitation phase. To judge the performance of this algorithm, a variety of real-world optimization problems taken from different disciplines are solved. According to the results obtained, this algorithm shows superior performance to those of state-of-the-art algorithms.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Editors	Yaochu Jin, Stefanos Kollias
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781509042401
ISBN (Print)	9781509042418
DOIs	https://doi.org/10.1109/SSCI.2016.7850258
Publication status	Published - 9 Feb 2017
Externally published	Yes
Event	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016

Publication series

Name	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16

Access to Document

10.1109/SSCI.2016.7850258

Cite this

Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2017). Two-phase differential evolution framework for solving optimization problems. In Y. Jin, & S. Kollias (Eds.), 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (pp. 1-8). [7850258] (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SSCI.2016.7850258

Sallam, Karam M. ; Elsayed, Saber M. ; Sarker, Ruhul A. et al. / Two-phase differential evolution framework for solving optimization problems. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. editor / Yaochu Jin ; Stefanos Kollias. United States : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1-8 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).

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title = "Two-phase differential evolution framework for solving optimization problems",

abstract = "Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design of an efficient selection mechanism to emphasize the best-performing DE variant has not yet been thoroughly explored. Motivated by this fact, in this paper, a new two-phase (exploration and exploitation) multi-operator DE algorithm is proposed. It starts with the exploration phase, dynamically placing emphasis on the best-performing DE based on two landscape indicators and its performance history, and then repeats this process during the exploitation phase. To judge the performance of this algorithm, a variety of real-world optimization problems taken from different disciplines are solved. According to the results obtained, this algorithm shows superior performance to those of state-of-the-art algorithms.",

keywords = "differential evolution, landscape indicators, multi-operator, real-world problems",

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

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

year = "2017",

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doi = "10.1109/SSCI.2016.7850258",

language = "English",

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Sallam, KM, Elsayed, SM, Sarker, RA & Essam, DL 2017, Two-phase differential evolution framework for solving optimization problems. in Y Jin & S Kollias (eds), 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7850258, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-8, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7850258

Two-phase differential evolution framework for solving optimization problems. / Sallam, Karam M.; Elsayed, Saber M.; Sarker, Ruhul A. et al.
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. ed. / Yaochu Jin; Stefanos Kollias. United States: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1-8 7850258 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).

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

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AU - Sarker, Ruhul A.

AU - Essam, Daryl L.

PY - 2017/2/9

Y1 - 2017/2/9

N2 - Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design of an efficient selection mechanism to emphasize the best-performing DE variant has not yet been thoroughly explored. Motivated by this fact, in this paper, a new two-phase (exploration and exploitation) multi-operator DE algorithm is proposed. It starts with the exploration phase, dynamically placing emphasis on the best-performing DE based on two landscape indicators and its performance history, and then repeats this process during the exploitation phase. To judge the performance of this algorithm, a variety of real-world optimization problems taken from different disciplines are solved. According to the results obtained, this algorithm shows superior performance to those of state-of-the-art algorithms.

AB - Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design of an efficient selection mechanism to emphasize the best-performing DE variant has not yet been thoroughly explored. Motivated by this fact, in this paper, a new two-phase (exploration and exploitation) multi-operator DE algorithm is proposed. It starts with the exploration phase, dynamically placing emphasis on the best-performing DE based on two landscape indicators and its performance history, and then repeats this process during the exploitation phase. To judge the performance of this algorithm, a variety of real-world optimization problems taken from different disciplines are solved. According to the results obtained, this algorithm shows superior performance to those of state-of-the-art algorithms.

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KW - landscape indicators

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BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

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PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Y2 - 6 December 2016 through 9 December 2016

ER -

Sallam KM, Elsayed SM, Sarker RA, Essam DL. Two-phase differential evolution framework for solving optimization problems. In Jin Y, Kollias S, editors, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. United States: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1-8. 7850258. (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). doi: 10.1109/SSCI.2016.7850258

Two-phase differential evolution framework for solving optimization problems

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