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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2019

31.08.2018 | Regular Paper

Flow Regime Algorithm (FRA): a physics-based meta-heuristics algorithm

verfasst von: Mojtaba Tahani, Narek Babayan

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2019

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Abstract

In this research study, a physics-based optimization algorithm, namely Flow Regime Algorithm (FRA) is proposed. The main sources of inspiration are classical fluid mechanics and flow regimes. The flow regime usually is being divided into two categories which are laminar and turbulent flows. Reynolds number is the parameter which defines that the flow regime is laminar or turbulent. In this research study, a similar number to Reynolds has been defined which indicates the search type (global or local) of the algorithm and is called search type factor. For the purpose of developing the local and global searches equations, the concept of boundary layer in fluid mechanics has been used. The performance of the proposed algorithm has been evaluated using 26 benchmark functions and has been compared with seven popular and well-known algorithms which are simulated annealing, particle swarm optimization, firefly algorithm, cuckoo search, flower pollination algorithm, krill herd and monarch butterfly. Finally, the heat wheel optimization problem and horizontal axis marine current turbine (tidal turbine) problem, which are real-case engineering problems, have been solved using FRA. The results indicated that FRA can be a great candidate in solving complex engineering problems.
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Metadaten
Titel
Flow Regime Algorithm (FRA): a physics-based meta-heuristics algorithm
verfasst von
Mojtaba Tahani
Narek Babayan
Publikationsdatum
31.08.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1253-3

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