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

doi:10.1016/j.knosys.2021.107529

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

2021-12-01

96

PRE

AI

王生生 (Shengsheng Wang)

DOI：10.1016/j.knosys.2021.107529

Original

Original request for help

Save

Abstract

En 中文

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. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Global optimization

Meta-heuristic algorithm

Kernel search algorithm

Swarm intelligence

Evolutionary algorithm

Economic emission dispatch problem

Dispatch

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

Abstract

Journal

Organization