A memetic cooperative co-evolution model for large scale continuous optimization

Yuan Sun; Michael Kirley; Saman K. Halgamuge

doi:10.1007/978-3-319-51691-2_25

A memetic cooperative co-evolution model for large scale continuous optimization

Yuan Sun, Michael Kirley, Saman K. Halgamuge

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

1 Citation (Scopus)

Abstract

Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorithm.

Original language	English
Title of host publication	Artificial Life and Computational Intelligence
Subtitle of host publication	Third Australasian Conference, ACALCI 2017, Proceedings
Editors	Markus Wagner, Xiaodong Li, Tim Hendtlass
Place of Publication	Cham Switzerland
Publisher	Springer-Verlag London Ltd.
Pages	291-300
Number of pages	10
Volume	10142
ISBN (Electronic)	9783319516912
ISBN (Print)	9783319516905
DOIs	https://doi.org/10.1007/978-3-319-51691-2_25
Publication status	Published - 2017
Externally published	Yes
Event	Australasian Conference on Artificial Life and Computational Intelligence 2017 - Deakin University, Geelong, Australia Duration: 31 Jan 2017 → 2 Feb 2017 Conference number: 3rd http://www.acalci.net/2017/ https://link.springer.com/book/10.1007/978-3-319-51691-2 (Springer Proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	10142
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Australasian Conference on Artificial Life and Computational Intelligence 2017
Abbreviated title	ACALCI 2017
Country	Australia
City	Geelong
Period	31/01/17 → 2/02/17
Other	ACACLI 2017 is co-located with the Australasian Computer Science Week (ACSW 2017), which will be held at Deakin University's Waterfront Campus, Geelong, which is about 70 kilometers west of Mebourne. 3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017
Internet address	http://www.acalci.net/2017/ https://link.springer.com/book/10.1007/978-3-319-51691-2 (Springer Proceedings)

Keywords

Continuous optimization problem
Cooperative co-evolution
Large scale global optimization
Memetic algorithm

Access to Document

10.1007/978-3-319-51691-2_25Licence: Unspecified

Link to publication in Scopus

Cite this

Sun, Y., Kirley, M., & Halgamuge, S. K. (2017). A memetic cooperative co-evolution model for large scale continuous optimization. In M. Wagner, X. Li, & T. Hendtlass (Eds.), Artificial Life and Computational Intelligence: Third Australasian Conference, ACALCI 2017, Proceedings (Vol. 10142, pp. 291-300). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10142). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-51691-2_25

Sun, Yuan ; Kirley, Michael ; Halgamuge, Saman K. / A memetic cooperative co-evolution model for large scale continuous optimization. Artificial Life and Computational Intelligence: Third Australasian Conference, ACALCI 2017, Proceedings. editor / Markus Wagner ; Xiaodong Li ; Tim Hendtlass. Vol. 10142 Cham Switzerland : Springer-Verlag London Ltd., 2017. pp. 291-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Sun, Y, Kirley, M & Halgamuge, SK 2017, A memetic cooperative co-evolution model for large scale continuous optimization. in M Wagner, X Li & T Hendtlass (eds), Artificial Life and Computational Intelligence: Third Australasian Conference, ACALCI 2017, Proceedings. vol. 10142, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10142, Springer-Verlag London Ltd., Cham Switzerland, pp. 291-300, Australasian Conference on Artificial Life and Computational Intelligence 2017, Geelong, Australia, 31/01/17. https://doi.org/10.1007/978-3-319-51691-2_25

A memetic cooperative co-evolution model for large scale continuous optimization. / Sun, Yuan; Kirley, Michael; Halgamuge, Saman K.

Artificial Life and Computational Intelligence: Third Australasian Conference, ACALCI 2017, Proceedings. ed. / Markus Wagner; Xiaodong Li; Tim Hendtlass. Vol. 10142 Cham Switzerland : Springer-Verlag London Ltd., 2017. p. 291-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10142).

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

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Sun Y, Kirley M, Halgamuge SK. A memetic cooperative co-evolution model for large scale continuous optimization. In Wagner M, Li X, Hendtlass T, editors, Artificial Life and Computational Intelligence: Third Australasian Conference, ACALCI 2017, Proceedings. Vol. 10142. Cham Switzerland: Springer-Verlag London Ltd. 2017. p. 291-300. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-51691-2_25