Neighborhood evidence-driven artificial bee colony algorithm with application for optimal operation of wet flue gas desulfurization system.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Neighborhood evidence-driven artificial bee colony algorithm with application for optimal operation of wet flue gas desulfurization system.
Συγγραφείς:	Liu, Chao¹ (AUTHOR), Fu, Yao¹ (AUTHOR), Su, Zhi-gang¹ (AUTHOR) zhigangsu@seu.edu.cn
Πηγή:	Intelligent Data Analysis. May2026, Vol. 30 Issue 3, p768-792. 25p.
Θεματικοί όροι:	Bees algorithm, Industrial applications, Flue gas desulfurization, Dempster-Shafer theory, Optimization algorithms, Swarm intelligence, Multisensor data fusion
Περίληψη:	Artificial bee colony (ABC) algorithm is a widely used swarm intelligence algorithm due to its simple structure, good robustness and strong exploration ability. However, the unbalanced exploration and exploitation capabilities restrict its performance. To tackle this problem, a Neighborhood Evidence-Driven Artificial Bee Colony (NEDABC) algorithm is proposed in the framework of theory of belief functions, integrating information fusion with ABC algorithm. In the onlooker bee phase, instead of being guided by the current optimal solution or specific elite solutions, onlooker bees select their search directions by fusing the comprehensive evidence provided by food sources within their neighborhood. Both short-range solutions and high-quality solutions can have a great impact on the selection of search direction. With this constructive search strategy, onlooker bees will enhance the exploration and exploitation of their nearby potential areas. Under the joint action of the population, the potential optimal region will soon be found and exploited. The proposed algorithm is tested on a set of benchmark functions and compared with several variants of ABC. Experimental results reveal its powerful abilities in achieving higher accuracy and faster convergency. In addition, the NEDABC algorithm is applied to operation optimization of a wet flue gas desulfurization system, demonstrating the practicability and efficiency of the proposed algorithm in engineering applications. [ABSTRACT FROM AUTHOR]
	Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων:	Business Source Index

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Items	– Name: Title Label: Title Group: Ti Data: Neighborhood evidence-driven artificial bee colony algorithm with application for optimal operation of wet flue gas desulfurization system. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Chao%22">Liu, Chao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Yao%22">Fu, Yao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Su%2C+Zhi-gang%22">Su, Zhi-gang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhigangsu@seu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. May2026, Vol. 30 Issue 3, p768-792. 25p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+applications%22">Industrial applications</searchLink><br /><searchLink fieldCode="DE" term="%22Flue+gas+desulfurization%22">Flue gas desulfurization</searchLink><br /><searchLink fieldCode="DE" term="%22Dempster-Shafer+theory%22">Dempster-Shafer theory</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Artificial bee colony (ABC) algorithm is a widely used swarm intelligence algorithm due to its simple structure, good robustness and strong exploration ability. However, the unbalanced exploration and exploitation capabilities restrict its performance. To tackle this problem, a Neighborhood Evidence-Driven Artificial Bee Colony (NEDABC) algorithm is proposed in the framework of theory of belief functions, integrating information fusion with ABC algorithm. In the onlooker bee phase, instead of being guided by the current optimal solution or specific elite solutions, onlooker bees select their search directions by fusing the comprehensive evidence provided by food sources within their neighborhood. Both short-range solutions and high-quality solutions can have a great impact on the selection of search direction. With this constructive search strategy, onlooker bees will enhance the exploration and exploitation of their nearby potential areas. Under the joint action of the population, the potential optimal region will soon be found and exploited. The proposed algorithm is tested on a set of benchmark functions and compared with several variants of ABC. Experimental results reveal its powerful abilities in achieving higher accuracy and faster convergency. In addition, the NEDABC algorithm is applied to operation optimization of a wet flue gas desulfurization system, demonstrating the practicability and efficiency of the proposed algorithm in engineering applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/1088467X251356155 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 768 Subjects: – SubjectFull: Bees algorithm Type: general – SubjectFull: Industrial applications Type: general – SubjectFull: Flue gas desulfurization Type: general – SubjectFull: Dempster-Shafer theory Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Multisensor data fusion Type: general Titles: – TitleFull: Neighborhood evidence-driven artificial bee colony algorithm with application for optimal operation of wet flue gas desulfurization system. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Chao – PersonEntity: Name: NameFull: Fu, Yao – PersonEntity: Name: NameFull: Su, Zhi-gang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1088467X Numbering: – Type: volume Value: 30 – Type: issue Value: 3 Titles: – TitleFull: Intelligent Data Analysis Type: main
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