Modified L-SHADE for Single Objective Real-Parameter Optimization

Jia Fong Yeh, Ting Yu Chen, Tsung-Che Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages381-386
Number of pages6
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - 2019 Jun 1
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 2019 Jun 102019 Jun 13

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period19/6/1019/6/13

Fingerprint

Parameter Optimization
Differential Evolution
Adaptive algorithms
Evolutionary algorithms
Polynomials
Data storage equipment
Population Size
Adaptive Algorithm
Digit
Evolutionary Algorithms
Mutation
Benchmark
Perturbation
Polynomial
History

Keywords

  • adaptive
  • differential evolution
  • success history

ASJC Scopus subject areas

  • Computational Mathematics
  • Modelling and Simulation

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 proceedingConference contribution

Yeh, JF, Chen, TY & Chiang, T-C 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, 19/6/10. https://doi.org/10.1109/CEC.2019.8789991
Yeh JF, Chen TY, Chiang T-C. Modified L-SHADE for Single Objective Real-Parameter Optimization. In 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). https://doi.org/10.1109/CEC.2019.8789991
Yeh, Jia Fong ; Chen, Ting Yu ; Chiang, Tsung-Che. / Modified L-SHADE for Single Objective Real-Parameter Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 381-386 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).
@inproceedings{b3ede3b9126543f185ea200994faef61,
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 Tsung-Che Chiang",
year = "2019",
month = "6",
day = "1",
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 - 2019/6/1

Y1 - 2019/6/1

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.

ER -