The Colony Predation Algorithm

Tu, J; Chen, H; Wang, M; Gandomi, AH

The Colony Predation Algorithm

Tu, J Chen, H Wang, M Gandomi, AH

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Journal of Bionic Engineering, 2021, 18, (3), pp. 674-710
Issue Date:: 2021-05-01

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Field	Value	Language
dc.contributor.author	Tu, J
dc.contributor.author	Chen, H
dc.contributor.author	Wang, M
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-03-31T03:52:16Z
dc.date.available	2022-03-31T03:52:16Z
dc.date.issued	2021-05-01
dc.identifier.citation	Journal of Bionic Engineering, 2021, 18, (3), pp. 674-710
dc.identifier.issn	1672-6529
dc.identifier.issn	1672-6529
dc.identifier.uri	http://hdl.handle.net/10453/155760
dc.description.abstract	This paper proposes a new stochastic optimizer called the Colony Predation Algorithm (CPA) based on the corporate predation of animals in nature. CPA utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target. Moreover, the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals’ selective abandonment behavior. This paper also presents a new way to deal with cross-border situations, whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm’s exploitation ability. The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm’s efficacy in optimizing engineering problems. The results show that the proposed algorithm exhibits competitive, superior performance in different search landscapes over the other algorithms. Moreover, the source code of the CPA will be publicly available after publication.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Journal of Bionic Engineering
dc.relation.isbasedon	10.1007/s42235-021-0050-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering
dc.subject.classification	Biomedical Engineering
dc.title	The Colony Predation Algorithm
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	0903 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-31T03:52:08Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	3

Abstract:

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm (CPA) based on the corporate predation of animals in nature. CPA utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target. Moreover, the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals’ selective abandonment behavior. This paper also presents a new way to deal with cross-border situations, whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm’s exploitation ability. The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm’s efficacy in optimizing engineering problems. The results show that the proposed algorithm exhibits competitive, superior performance in different search landscapes over the other algorithms. Moreover, the source code of the CPA will be publicly available after publication.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155760