Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization.

Autor: Adegboye OR; Management Information Systems, University of Mediterranean Karpasia, Mersin-10, Turkey., Feda AK; Management Information System Department, European University of Lefke, Mersin-10, Turkey., Ojekemi OS; Engineering Management, University of Mediterranean Karpasia, Mersin-10, Turkey., Agyekum EB; Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, 19 Mira Street, Yekaterinburg, Russia, 620002., Hussien AG; Department of Computer and Information Science, Linköping University, Linköping, Sweden. aga08@fayoum.edu.eg.; Faculty of Science, Fayoum University, El Faiyûm, Egypt. aga08@fayoum.edu.eg.; Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan. aga08@fayoum.edu.eg.; MEU Research Unit, Middle East University, Amman, 11831, Jordan. aga08@fayoum.edu.eg., Kamel S; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Feb 26; Vol. 14 (1), pp. 4660. Date of Electronic Publication: 2024 Feb 26.
DOI: 10.1038/s41598-024-55040-6
Abstrakt: The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it's called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.
(© 2024. The Author(s).)
Databáze: MEDLINE
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