Novel genetic algorithms (GAs) are developed by using state-of-the-art selection and crossover operators, e.g., rank selection or tournament selection instead of the traditional roulette (fitness proportionate (FP)) selection operator and novel crossover and mutation operators by considering the chromosomes as permutations (which is a specific feature of the loading pattern (LP) problem). The algorithm is applied to a representative model of a modern pressurized water reactor (PWR) core and implemented using a single objective fitness function (FF), i.e., keff. The results obtained for some reference cases using this setup are excellent. They are obtained using a tournament selection operator with a linear ranking (LR) selection probability method and a new geometric crossover operator that allows for geometrical, rather than random, swaps of gene segments between the chromosomes and control over the sizes of the swapped segments. Finally, the effect of boundary conditions (BCs) on the symmetry of the obtained best solutions is studied and the validity of the “symmetric loading patterns” assumption is tested.

References

1.
Turinsky
,
P. J.
,
2005
, “
Nuclear Fuel Management Optimization: A Work in Progress
,”
Nucl. Technol.
,
151
(1), pp.
3
8
.
2.
Turinsky
,
P. J.
,
Keller
,
P. M.
, and
Abdel-Khalik
,
H. S.
,
2005
, “
Evolution of Nuclear Fuel Management and Reactor Operational Aid Tools
,”
Nucl. Eng. Technol.
,
37
(
1
), pp.
79
90
.
3.
Holland
,
J. H.
,
1975
,
Adaptation in Natural and Artificial Systems
,
University of Michigan Press
,
Ann Arbor, MI
.
4.
Goldberg
,
D. E.
,
1989
,
Genetic Algorithms in Search, Optimization and Machine Learning
, 1st ed.,
Addison-Wesley Longman Publishing
,
Boston, MA
.
5.
Parks
,
G. T.
,
1996
, “
Multiobjective PWR Reload Core Design by Nondominated Genetic Algorithm Search
,”
Nucl. Sci. Eng.
,
124
(1), pp.
178
187
.
6.
Haibach
,
B. V.
, and
Feltus
,
M. A.
,
1997
, “
A Study on the Optimization of Integral Fuel Burnable Absorbers Using the Genetic Algorithm Based Cigaro Fuel Management System
,”
Ann. Nucl. Energy
,
24
(
6
), pp.
439
448
.
7.
Parks
,
G. T.
, and
Miller
,
I.
,
1998
,
Selective Breeding in a Multiobjective Genetic Algorithm
,
Springer
,
Berlin
, pp.
250
259
.
8.
Chapot
,
J. L. C.
,
Silva
,
F. C. D.
, and
Schirru
,
R.
,
1999
, “
A New Approach to the Use of Genetic Algorithms to Solve the Pressurized Water Reactor's Fuel Management Optimization Problem
,”
Ann. Nucl. Energy
,
26
(
7
), pp.
641
655
.
9.
Toshinsky
,
V. G.
,
Sekimoto
,
H.
, and
Toshinsky
,
G. I.
,
1999
, “
Multiobjective Fuel Management Optimization for Self-Fuel-Providing LMFBR Using Genetic Algorithms
,”
Ann. Nucl. Energy
,
26
(
9
), pp.
783
802
.
10.
Toshinsky
,
V. G.
,
Sekimoto
,
H.
, and
Toshinsky
,
G. I.
,
2000
, “
A Method to Improve Multiobjective Genetic Algorithm Optimization of a Self-Fuel-Providing LMFBR by Niche Induction Among Nondominated Solutions
,”
Ann. Nucl. Energy
,
27
(
5
), pp.
397
410
.
11.
Hongchun
,
W.
,
2001
, “
Pressurized Water Reactor Reloading Optimization Using Genetic Algorithms
,”
Ann. Nucl. Energy
,
28
(
13
), pp.
1329
1341
.
12.
Gang
,
P.
,
Feng
,
P.
, and
Rong
,
F.
,
2002
, “
Application of Genetic Algorithm in Research and Test Reactor Core Loading Pattern Optimization
,”
PHYSOR 2002
, Seoul, Korea, Paper No. 8A-03.
13.
Erdogan
,
A.
, and
Geckinli
,
M.
,
2003
, “
A PWR Reload Optimisation Code (XCore) Using Artificial Neural Networks and Genetic Algorithms
,”
Ann. Nucl. Energy
,
30
(
1
), pp.
35
53
.
14.
Pereiraa
,
C. M.
, and
Lapa
,
C. M.
,
2003
, “
Coarse-Grained Parallel Genetic Algorithm Applied to a Nuclear Reactor Core Design Optimization Problem
,”
Ann. Nucl. Energy
,
30
(
5
), pp.
555
565
.
15.
Ortiz
,
J. J.
, and
Requena
,
I.
,
2004
, “
An Order Coding Genetic Algorithm to Optimize Fuel Reloads in a Nuclear Boiling Water Reactor
,”
Nucl. Sci. Eng.
,
146
(
1
), pp.
88
98
.
16.
Alim
,
F.
,
Ivanov
,
K.
, and
Levine
,
S. H.
,
2008
, “
New Genetic Algorithms (GA) to Optimize PWR Reactors Part I: Loading Pattern and Burnable Poison Placement Optimization Techniques for PWRs
,”
Ann. Nucl. Energy
,
35
(
1
), pp.
93
112
.
17.
Alim
,
F.
,
Ivanov
,
K.
,
Yilmaz
,
S.
, and
Levine
,
S. H.
,
2008
, “
New Genetic Algorithms (GA) to Optimize PWR Reactors Part II: Simultaneous Optimization of Loading Pattern and Burnable Poison Placement for the TMI-1 Reactor
,”
Ann. Nucl. Energy
,
35
(
1
), pp.
113
120
.
18.
Khoshahval
,
F.
,
Minuchehr
,
H.
, and
Zolfaghari
,
A.
,
2011
, “
Performance Evaluation of PSO and GA in PWR Core Loading Pattern Optimization
,”
Nucl. Eng. Des.
,
241
(
3
), pp.
799
808
.
19.
Rahmania
,
Y.
,
Pazirandeh
,
A.
,
Ghofrani
,
M. B.
, and
Sadighi
,
M.
,
2013
, “
Using a Combination of Weighting Factors, Genetic Algorithm and Ant Colony Methods to Speed up the Reloading Pattern Optimization of VVER-1000 Reactors
,” Transactions of the Conference Safety Assurance of NPP With WWER Vol. 1, V. Mokhov, S. Sorokin, S. Titova, and E. Serdobintseva, eds., JSC OKB GIDROPRESS, Podolsk, Russia.
20.
Zameer
,
A.
,
Mirza
,
S. M.
, and
Mirza
,
N. M.
,
2014
, “
Core Loading Pattern Optimization of a Typical Two-Loop 300 MWe PWR Using Simulated Annealing (SA), Novel Crossover Genetic Algorithms (GA) and Hybrid GA(SA) Schemes
,”
Ann. Nucl. Energy
,
65
, pp.
122
131
.
21.
Mitsubishi Heavy Industries, Ltd., 2013, “APWR Design Control Document (DCD),” Mitsubishi Heavy Industries, Ltd., Tokyo, Japan, Tier 2, Chap. 4, Rev. 4.
22.
Grundmann
,
U.
,
Rohde
,
U.
, and
Mittag
,
S.
,
2000
, “
DYN3D–Three-Dimensional Core Model for Steady State and Transient Analysis of Thermal Reactors
,”
PHYSOR 2000
, Pittsburgh, PA, Paper No. 155.
23.
Stammler, R. J.,
2003
, “HELIOS Methods,” Studsvik Scandpower, Kjeller, Norway.
You do not currently have access to this content.