An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance

Lu, Z; Qi, S; Zhang, J; Cai, Y; Guo, X; Luo, S

An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance

Lu, Z Qi, S Zhang, J

Cai, Y Guo, X Luo, S

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Soft Computing, 2022, 26, (1), pp. 69-87
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lu, Z
dc.contributor.author	Qi, S
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722
dc.contributor.author	Cai, Y
dc.contributor.author	Guo, X
dc.contributor.author	Luo, S
dc.date.accessioned	2023-03-20T02:09:29Z
dc.date.available	2023-03-20T02:09:29Z
dc.date.issued	2022-01-01
dc.identifier.citation	Soft Computing, 2022, 26, (1), pp. 69-87
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/167690
dc.description.abstract	This paper puts forward an improved multi-objective bacterial colony chemotaxis (MOBCC) algorithm based on Pareto dominance. A time-varying step size tactic is adopted to increase the global and local searching abilities of the improved MOBCC algorithm. An external archive is created to keep previously found Pareto optimal solutions. A non-dominated sorting method integrating crowding distance assignment is applied to enhance the time efficiency of the improved MOBCC algorithm. A hybrid method combining bacterial individual mutation, oriented mutation of bacterial colony and local search of external archive is applied to enhance the convergence of the algorithm and maintain the diversity of solution set. The framework of MOEAs based on Pareto dominance is integrated into the improved MOBCC algorithm properly through replacements of the bacterial individuals in the bacterial colony, archive operation, and updating of the bacterial colony. The improved MOBCC algorithm is compared with three common multi-objective optimization algorithms SPEA2, NSGA-II and MOEA/D on fifteen test problems and evolution of optimization, and the experimental results confirm the validity of the improved MOBCC algorithm. Furthermore, the effects of the improved MOBCC algorithm’s parameters on the performance of the improved MOBCC algorithm are analyzed.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-021-06467-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-20T02:09:28Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	1

Abstract:

This paper puts forward an improved multi-objective bacterial colony chemotaxis (MOBCC) algorithm based on Pareto dominance. A time-varying step size tactic is adopted to increase the global and local searching abilities of the improved MOBCC algorithm. An external archive is created to keep previously found Pareto optimal solutions. A non-dominated sorting method integrating crowding distance assignment is applied to enhance the time efficiency of the improved MOBCC algorithm. A hybrid method combining bacterial individual mutation, oriented mutation of bacterial colony and local search of external archive is applied to enhance the convergence of the algorithm and maintain the diversity of solution set. The framework of MOEAs based on Pareto dominance is integrated into the improved MOBCC algorithm properly through replacements of the bacterial individuals in the bacterial colony, archive operation, and updating of the bacterial colony. The improved MOBCC algorithm is compared with three common multi-objective optimization algorithms SPEA2, NSGA-II and MOEA/D on fifteen test problems and evolution of optimization, and the experimental results confirm the validity of the improved MOBCC algorithm. Furthermore, the effects of the improved MOBCC algorithm’s parameters on the performance of the improved MOBCC algorithm are analyzed.

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http://hdl.handle.net/10453/167690