Modified L-SHADE for Single Objective Real-Parameter Optimization

Jia Fong Yeh; Ting Yu Chen; Tsung Che Chiang

doi:10.1109/CEC.2019.8789991

Modified L-SHADE for Single Objective Real-Parameter Optimization

Jia Fong Yeh, Ting Yu Chen, Tsung Che Chiang

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

In this paper we address single objective real parameter optimization by using differential evolution (DE). L-SHADE is a well-known DE with success history-based adaptation and linear population size reduction. We propose a modified L-SHADE (mL-SHADE), in which three modifications are made: (1) removal of the terminal value, (2) addition of polynomial mutation, and (3) proposal of a memory perturbation mechanism. Performance of the proposed mL-SHADE is verified by using ten benchmark functions in the CEC2019 100-Digit Challenge. The results show that mL-SHADE achieves a higher score than seven state-of-the-art adaptive evolutionary algorithms.

Original language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	381-386
Number of pages	6
ISBN (Electronic)	9781728121536
DOIs	https://doi.org/10.1109/CEC.2019.8789991
Publication status	Published - 2019 Jun
Event	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand Duration: 2019 Jun 10 → 2019 Jun 13

Publication series

Name	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference	2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/Territory	New Zealand
City	Wellington
Period	2019/06/10 → 2019/06/13

Keywords

adaptive
differential evolution
success history

ASJC Scopus subject areas

Computational Mathematics
Modelling and Simulation

Access to Document

10.1109/CEC.2019.8789991

Cite this

Yeh, J. F., Chen, T. Y., & Chiang, T. C. (2019). Modified L-SHADE for Single Objective Real-Parameter Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 381-386). [8789991] (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8789991

Modified L-SHADE for Single Objective Real-Parameter Optimization. / Yeh, Jia Fong; Chen, Ting Yu; Chiang, Tsung Che.

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 381-386 8789991 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yeh, JF, Chen, TY & Chiang, TC 2019, Modified L-SHADE for Single Objective Real-Parameter Optimization. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8789991, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 381-386, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 2019/06/10. https://doi.org/10.1109/CEC.2019.8789991

@inproceedings{f6aee0b66b7f4497a63dd2f99295f35d,

title = "Modified L-SHADE for Single Objective Real-Parameter Optimization",

abstract = "In this paper we address single objective real parameter optimization by using differential evolution (DE). L-SHADE is a well-known DE with success history-based adaptation and linear population size reduction. We propose a modified L-SHADE (mL-SHADE), in which three modifications are made: (1) removal of the terminal value, (2) addition of polynomial mutation, and (3) proposal of a memory perturbation mechanism. Performance of the proposed mL-SHADE is verified by using ten benchmark functions in the CEC2019 100-Digit Challenge. The results show that mL-SHADE achieves a higher score than seven state-of-the-art adaptive evolutionary algorithms.",

keywords = "adaptive, differential evolution, success history",

author = "Yeh, {Jia Fong} and Chen, {Ting Yu} and Chiang, {Tsung Che}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Congress on Evolutionary Computation, CEC 2019 ; Conference date: 10-06-2019 Through 13-06-2019",

year = "2019",

month = jun,

doi = "10.1109/CEC.2019.8789991",

language = "English",

series = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "381--386",

booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",

}

TY - GEN

T1 - Modified L-SHADE for Single Objective Real-Parameter Optimization

AU - Yeh, Jia Fong

AU - Chen, Ting Yu

AU - Chiang, Tsung Che

PY - 2019/6

Y1 - 2019/6

N2 - In this paper we address single objective real parameter optimization by using differential evolution (DE). L-SHADE is a well-known DE with success history-based adaptation and linear population size reduction. We propose a modified L-SHADE (mL-SHADE), in which three modifications are made: (1) removal of the terminal value, (2) addition of polynomial mutation, and (3) proposal of a memory perturbation mechanism. Performance of the proposed mL-SHADE is verified by using ten benchmark functions in the CEC2019 100-Digit Challenge. The results show that mL-SHADE achieves a higher score than seven state-of-the-art adaptive evolutionary algorithms.

AB - In this paper we address single objective real parameter optimization by using differential evolution (DE). L-SHADE is a well-known DE with success history-based adaptation and linear population size reduction. We propose a modified L-SHADE (mL-SHADE), in which three modifications are made: (1) removal of the terminal value, (2) addition of polynomial mutation, and (3) proposal of a memory perturbation mechanism. Performance of the proposed mL-SHADE is verified by using ten benchmark functions in the CEC2019 100-Digit Challenge. The results show that mL-SHADE achieves a higher score than seven state-of-the-art adaptive evolutionary algorithms.

KW - adaptive

KW - differential evolution

KW - success history

UR - http://www.scopus.com/inward/record.url?scp=85071334850&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071334850&partnerID=8YFLogxK

U2 - 10.1109/CEC.2019.8789991

DO - 10.1109/CEC.2019.8789991

M3 - Conference contribution

AN - SCOPUS:85071334850

T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

SP - 381

EP - 386

BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019

Y2 - 10 June 2019 through 13 June 2019

ER -

Modified L-SHADE for Single Objective Real-Parameter Optimization

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this