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FUELGEN: effective evolutionary design of refuellings for pressurized water reactors

FUELGEN: effective evolutionary design of refuellings for pressurized water reactors

Zhao, J., Knight, B., Nissan, E. and Soper, A. ORCID: 0000-0002-0901-9803 (1998) FUELGEN: effective evolutionary design of refuellings for pressurized water reactors. Computers and Artificial Intelligence, 17 (2-3). pp. 105-125. ISSN 0232-0274

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Abstract

The paper describes the design of an efficient and robust genetic algorithm for the nuclear fuel loading problem (i.e., refuellings: the in-core fuel management problem) - a complex combinatorial, multimodal optimisation., Evolutionary computation as performed by FUELGEN replaces heuristic search of the kind performed by the FUELCON expert system (CAI 12/4), to solve the same problem.

In contrast to the traditional genetic algorithm which makes strong requirements on the representation used and its parameter setting in order to be efficient, the results of recent research results on new, robust genetic algorithms show that representations unsuitable for the traditional genetic algorithm can still be used to good effect with little parameter adjustment. The representation presented here is a simple symbolic one with no linkage attributes, making the genetic algorithm particularly easy to apply to fuel loading problems with differing core structures and assembly inventories. A nonlinear fitness function has been constructed to direct the search efficiently in the presence of the many local optima that result from the constraint on solutions.

Item Type: Article
Additional Information: [1] Computers and Artificial Intelligence has been published as Computing and Informatics since 2000.
Uncontrolled Keywords: nuclear engineering
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 08:59
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/101

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