Multi-strategy enhanced kernel search optimization and its application in economic emission dispatch problems

Author:

Dong Ruyi1,Liu Yanan1,Wang Siwen2,Heidari Ali Asghar3ORCID,Wang Mingjing4,Chen Yi5,Wang Shuihua6,Chen Huiling5ORCID,Zhang Yudong6ORCID

Affiliation:

1. College of Information and Control Engineering, Jilin Institute of Chemical Technology , Jilin , China

2. Affiliated Middle School to Jilin University , Changchun   China

3. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran , Tehran , Iran

4. School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou, 325000 , China

5. Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University , Wenzhou 325035 , China

6. School of Computing and Mathematical Sciences, University of Leicester , Leicester , LE1 7RH, UK

Abstract

Abstract The Kernel Search Optimizer (KSO) is a recent metaheuristic optimization algorithm that has been proposed in recent years. The KSO is based on kernel theory, eliminating the need for hyper-parameter adjustments, and demonstrating excellent global search capabilities. However, the original KSO exhibits insufficient accuracy in local search, and there is a high probability that it may fail to achieve local optimization in complex tasks. Therefore, this paper proposes a Multi-Strategy Enhanced Kernel Search Optimizer (MSKSO) to enhance the local search ability of the KSO. The MSKSO combines several control strategies, including chaotic initialization, chaotic local search mechanisms, the High-Altitude Walk Strategy (HWS), and the Levy Flight (LF), to effectively balance exploration and exploitation. The MSKSO is compared with ten well-known algorithms on fifty benchmark test functions to validate its performance, including single-peak, multi-peak, separable variable, and non-separable variable functions. Additionally, the MSKSO is applied to two real engineering economic emission dispatch (EED) problems in power systems. Experimental results demonstrate that the performance of the MSKSO nearly optimizes that of other well-known algorithms and achieves favorable results on the EED problem. These case studies verify that the MSKSO outperforms other algorithms and can serve as an effective optimization tool.

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computer Graphics and Computer-Aided Design,Human-Computer Interaction,Engineering (miscellaneous),Modeling and Simulation,Computational Mechanics

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