Paper Info
Title
Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem
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.
Year
DOI
Venue
2021
10.1016/j.knosys.2021.107529
Knowledge-Based Systems
Keywords
DocType
Volume
Global optimization,Meta-heuristic algorithm,Kernel search algorithm,Swarm intelligence,Evolutionary algorithm,Economic emission dispatch problem,Dispatch
Journal
233
ISSN
Citations 
PageRank 
0950-7051
1
0.34
References 
Authors
102
6
Search Limit
100102
Name
Order
Citations
PageRank
Ruyi Dong121.70
Huiling Chen240228.49
Ali Asghar Heidari343917.20
Hamza Turabieh413611.41
Majdi Mafarja557420.00
Shengsheng Wang69817.51