Multi-strategy differential evolution

A. Yaman; G. Iacca; M. Coler; G.H.L. Fletcher; M. Pechenizkiy

doi:10.1007/978-3-319-77538-8_42

Multi-strategy differential evolution

A. Yaman, G. Iacca, M. Coler, G.H.L. Fletcher, M. Pechenizkiy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

1 Downloads (Pure)

Abstract

We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art

Original language	English
Title of host publication	Applications of Evolutionary Computation
Subtitle of host publication	21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings
Editors	Kevin Sim, Paul Kaufmann
Place of Publication	Dordrecht
Publisher	Springer
Pages	617-633
Number of pages	17
ISBN (Electronic)	978-3-319-77538-8
ISBN (Print)	978-3-319-77537-1
DOIs	https://doi.org/10.1007/978-3-319-77538-8_42
Publication status	Published - 4 Mar 2018
Event	21st International Conference on the Applications of Evolutionary Computing (EvoApplications 2018) - Parma, Italy Duration: 4 Apr 2018 → 6 Apr 2018 Conference number: 21 http://www.evostar.org/2018/cfp_evoapps.php

Publication series

Name	LNCS
Volume	10784

Conference

Conference	21st International Conference on the Applications of Evolutionary Computing (EvoApplications 2018)
Abbreviated title	EvoApplications 2018
Country/Territory	Italy
City	Parma
Period	4/04/18 → 6/04/18
Other	Held as part of the EvoStar 2018 event in Parma, Italy, April 2018
Internet address	http://www.evostar.org/2018/cfp_evoapps.php

Keywords

Continuous optimization
Differential evolution
Parameter control
Strategy adaptation

Access to Document

10.1007/978-3-319-77538-8_42

Cite this

Yaman, A., Iacca, G., Coler, M., Fletcher, G. H. L., & Pechenizkiy, M. (2018). Multi-strategy differential evolution. In K. Sim, & P. Kaufmann (Eds.), Applications of Evolutionary Computation: 21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings (pp. 617-633). (LNCS; Vol. 10784). Springer. https://doi.org/10.1007/978-3-319-77538-8_42

@inproceedings{21527a8c0e6f4132ae38e73b60229dbf,

title = "Multi-strategy differential evolution",

abstract = "We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art",

keywords = "Continuous optimization, Differential evolution, Parameter control, Strategy adaptation",

author = "A. Yaman and G. Iacca and M. Coler and G.H.L. Fletcher and M. Pechenizkiy",

year = "2018",

month = mar,

day = "4",

doi = "10.1007/978-3-319-77538-8_42",

language = "English",

isbn = "978-3-319-77537-1",

series = "LNCS",

publisher = "Springer",

pages = "617--633",

editor = "Kevin Sim and Paul Kaufmann",

booktitle = "Applications of Evolutionary Computation",

address = "Germany",

note = "21st International Conference on the Applications of Evolutionary Computing (EvoApplications 2018), EvoApplications 2018 ; Conference date: 04-04-2018 Through 06-04-2018",

url = "http://www.evostar.org/2018/cfp_evoapps.php",

}

Yaman, A, Iacca, G, Coler, M, Fletcher, GHL & Pechenizkiy, M 2018, Multi-strategy differential evolution. in K Sim & P Kaufmann (eds), Applications of Evolutionary Computation: 21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings. LNCS, vol. 10784, Springer, Dordrecht, pp. 617-633, 21st International Conference on the Applications of Evolutionary Computing (EvoApplications 2018), Parma, Italy, 4/04/18. https://doi.org/10.1007/978-3-319-77538-8_42

Multi-strategy differential evolution. / Yaman, A.; Iacca, G.; Coler, M. et al.
Applications of Evolutionary Computation: 21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings. ed. / Kevin Sim; Paul Kaufmann. Dordrecht: Springer, 2018. p. 617-633 (LNCS; Vol. 10784).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Multi-strategy differential evolution

AU - Yaman, A.

AU - Iacca, G.

AU - Coler, M.

AU - Fletcher, G.H.L.

AU - Pechenizkiy, M.

N1 - Conference code: 21

PY - 2018/3/4

Y1 - 2018/3/4

N2 - We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art

AB - We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art

KW - Continuous optimization

KW - Differential evolution

KW - Parameter control

KW - Strategy adaptation

UR - http://www.scopus.com/inward/record.url?scp=85044055340&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-77538-8_42

DO - 10.1007/978-3-319-77538-8_42

M3 - Conference contribution

SN - 978-3-319-77537-1

T3 - LNCS

SP - 617

EP - 633

BT - Applications of Evolutionary Computation

A2 - Sim, Kevin

A2 - Kaufmann, Paul

PB - Springer

CY - Dordrecht

T2 - 21st International Conference on the Applications of Evolutionary Computing (EvoApplications 2018)

Y2 - 4 April 2018 through 6 April 2018

ER -

Multi-strategy differential evolution

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this