Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a...

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Veröffentlicht in:Scientific reports Jg. 13; H. 1; S. 15909 - 40
Hauptverfasser: Ravichandran, Sowmya, Manoharan, Premkumar, Jangir, Pradeep, Selvarajan, Shitharth
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 23.09.2023
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/166
639/705
Algorithms
Capacitance
Design engineering
Design optimization
Humanities and Social Sciences
multidisciplinary
Optimization algorithms
Physics
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-42969-3