Improving an immune-inspired algorithm by linear regression: A case study on network reliability

Bio-inspired algorithms are widely and successfully used in solving complex optimization problems. However, a common challenge faced by these algorithms is getting stuck in local search spaces and failing to explore new regions to further enhance the solution. Such a limitation is often attributed t...

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Veröffentlicht in:	Knowledge-based systems Jg. 299; S. 112034
Hauptverfasser:	Cutello, Vincenzo, Pavone, Mario, Zito, Francesco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 05.09.2024
Schlagworte:	Dynamic mutation rate Immune algorithm Machine learning Metaheuristic Network optimization Network reliability maximization Ordinary least squares regression Immune algorithm Network reliability maximization Metaheuristic Ordinary least squares regression Dynamic mutation rate Network optimization Machine learning
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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