An integer coded genetic algorithm based on a replacement procedure for designing operational control architectures of critical systems

In this paper, a genetic algorithm (GA) method for the design of the operational architecture of a control system is presented. It provides from the knowledge of some characteristics of the functions that the control system must ensure, an allocation solution of these functions on industrial control...

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Vydané v:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) s. 1 - 6
Hlavní autori: Nasri, Imed, Petin, Jean-Francois, Simon, Frederique Bicking
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2015
Predmet:
Computer architecture
Genetic algorithms
Optimization
Resource management
Safety
Sociology
Statistics
ISSN:1946-0740
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Shrnutí:In this paper, a genetic algorithm (GA) method for the design of the operational architecture of a control system is presented. It provides from the knowledge of some characteristics of the functions that the control system must ensure, an allocation solution of these functions on industrial controllers while satisfying capabilities, compatibility and exclusion constraints in order to minimize the controllers number. An integer coded GA with solution representation special to the allocation of functions structure is proposed for solving the allocation problem. It is a suitably modified version of the GA for the generalised allocation problem of Chu and Beasley [1]. A uniform crossover with random binary mask and uniform mutation are employed. The objective of this paper is not to obtain a global minimum value of required controllers number but to design varied operational architectures with different solutions allowing in future work the minimization or the evaluation of other optimization criteria such as temporal and dependability performances. For these reasons, a replacement procedure is introduced in the algorithm. The proposed heuristic is programmed in MATLAB R2013a. In order to illustrate the performance of the proposed methodology, a simulation example is given.
ISSN:1946-0740
DOI:10.1109/ETFA.2015.7301428