Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem

In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms suffer from the shortcoming that multiple hyperparameters need to be set carefully. Therefore, to solve the problem, the kernel search optimiza...

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Vydáno v:	Knowledge-based systems Ročník 233; s. 107529
Hlavní autoři:	Dong, Ruyi, Chen, Huiling, Heidari, Ali Asghar, Turabieh, Hamza, Mafarja, Majdi, Wang, Shengsheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 05.12.2021
Témata:	Dispatch Economic emission dispatch problem Evolutionary algorithm Global optimization Kernel search algorithm Meta-heuristic algorithm Swarm intelligence Global optimization Meta-heuristic algorithm Evolutionary algorithm Dispatch Swarm intelligence Economic emission dispatch problem Kernel search algorithm
ISSN:	0950-7051, 1872-7409
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms suffer from the shortcoming that multiple hyperparameters need to be set carefully. Therefore, to solve the problem, the kernel search optimization (KSO) algorithm inspired by the kernel method has been proposed. KSO can simplify the optimization process by transforming the optimization process of nonlinear function into the linear optimization process. Despite its advantage, the original KSO requires a large amount of computation, and has no powerful exploitation search, resulting in its inability to obtain more accurate results. In the present study, a local search of the hill-climbing algorithm is adopted, and the calculation of the kernel parameter is simplified to improve the original KSO. In an experiment using 50 benchmark functions, the new algorithm outperformed KSO and some well-known algorithms in accuracy and running time. Moreover, when applied in the real-world economic emission dispatch problem, the improved algorithm achieved a better performance than other algorithms compared. An online repository will support this research at https://aliasgharheidari.com. •The kernel parameter’s calculation is simplified to improve KSO algorithm.•A local search of the hill-climbing algorithm is utilized for KSO’s exploitation.•IKSO is compared with some classic and popular MAs on benchmark functions.•The performance of IKSO is evaluated in different dimensions and types.•IKSO has achieved a much better performance than other literature MAs in EED problem.
AbstractList	In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms suffer from the shortcoming that multiple hyperparameters need to be set carefully. Therefore, to solve the problem, the kernel search optimization (KSO) algorithm inspired by the kernel method has been proposed. KSO can simplify the optimization process by transforming the optimization process of nonlinear function into the linear optimization process. Despite its advantage, the original KSO requires a large amount of computation, and has no powerful exploitation search, resulting in its inability to obtain more accurate results. In the present study, a local search of the hill-climbing algorithm is adopted, and the calculation of the kernel parameter is simplified to improve the original KSO. In an experiment using 50 benchmark functions, the new algorithm outperformed KSO and some well-known algorithms in accuracy and running time. Moreover, when applied in the real-world economic emission dispatch problem, the improved algorithm achieved a better performance than other algorithms compared. An online repository will support this research at https://aliasgharheidari.com. •The kernel parameter’s calculation is simplified to improve KSO algorithm.•A local search of the hill-climbing algorithm is utilized for KSO’s exploitation.•IKSO is compared with some classic and popular MAs on benchmark functions.•The performance of IKSO is evaluated in different dimensions and types.•IKSO has achieved a much better performance than other literature MAs in EED problem.
ArticleNumber	107529
Author	Heidari, Ali Asghar Turabieh, Hamza Chen, Huiling Mafarja, Majdi Dong, Ruyi Wang, Shengsheng
Author_xml	– sequence: 1 givenname: Ruyi surname: Dong fullname: Dong, Ruyi email: dongruyi@163.com organization: College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, China – sequence: 2 givenname: Huiling surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com organization: College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China – sequence: 3 givenname: Ali Asghar surname: Heidari fullname: Heidari, Ali Asghar email: aliasghar68@gmail.com organization: College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China – sequence: 4 givenname: Hamza orcidid: 0000-0002-8103-563X surname: Turabieh fullname: Turabieh, Hamza email: h.turabieh@tu.edu.sa organization: Department of Information Technology, College of Computers and Information Technology, P.O. Box 11099, Taif 21944, Taif University, Taif, Saudi Arabia – sequence: 5 givenname: Majdi orcidid: 0000-0002-0387-8252 surname: Mafarja fullname: Mafarja, Majdi email: mmafarja@birzeit.edu organization: Department of Computer Science, Birzeit University, POBox 14, West Bank, Palestine – sequence: 6 givenname: Shengsheng surname: Wang fullname: Wang, Shengsheng email: wss@jlu.edu.cn organization: College of Computer Science and Technology, Jilin University, Changchun, China
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Snippet	In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms...
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SubjectTerms	Dispatch Economic emission dispatch problem Evolutionary algorithm Global optimization Kernel search algorithm Meta-heuristic algorithm Swarm intelligence
Title	Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem
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