This paper presents the use of multiobjective evolutionary algorithms for the optimal geometrical design of a pin-fin heat sink. The multiobjective design problem is posed to minimize two conflicting objectives: the junction temperature and the fan pumping power of the heat sink. The design variables are mixed integer/continuous. The encoding/decoding process for this mixed integer/continuous design variables is detailed. The multiobjective optimizers employed to solve the design problem are population-based incremental learning, strength Pareto evolutionary algorithm, particles swarm optimization, and archived multiobjective simulated annealing. The approximate Pareto fronts obtained from using the various optimizers are compared based upon the hypervolume and generational distance indicators. From the results, population-based incremental learning (PBIL) outperforms the others. The new design approach is said to be superior to a classical design approach. It is also illustrated that the proposed multiobjective design process leads to better design compared to the current commercial pin-fin heat sinks.

References

References
1.
Srisomporn
,
S.
, and
Bureerat
,
S.
, 2008, “
Geometrical Design of Plate-Fin Heat Sinks Using Hybridization of MOEA and RSM
,”
IEEE Trans. Compon. Packag. Technol.
,
31
, pp.
351
360
.
2.
Bureerat
,
S.
, and
Srisomporn
,
S.
, 2010, “
Optimum Plate-Fin Heat Sinks by Using a Multi-Objective Evolutionary Algorithm
,”
Eng. Optimiz.
,
42
, pp.
305
323
.
3.
Park
,
K.
,
Oh
,
P. K.
, and
Lim
,
H. J.
, 2006, “
The Application of the CFD and Kriging Method to an Optimization of Heat Sink
,”
Int. J. Heat Mass Transfer
,
49
, pp.
3439
3447
.
4.
Chiang
,
K. T.
, 2005, “
Optimization of the Design Parameters of Parallel-Plain Fin Heat Sink Module Cooling Phenomenon Based on the Taguchi Method
,”
Int. Commun. Heat Mass Transfer
,
32
, pp.
1193
1201
.
5.
Iyengar
,
M.
, and
Bar-Cohen
,
A.
, 2005, “
Least-Energy Optimisation of Forced Convection Plate-Fin Heat Sinks
,”
IEEE Trans. Compon. Packag. Technol.
,
26
, pp.
62
70
.
6.
Ndao
,
S.
,
Peles
,
Y.
, and
Jensen
,
M. K.
, 2009, “
Multi-Objective Thermal Design Optimization and Comparative Analysis of Electronics Cooling Technologies
,”
Int. J. Heat Mass Transfer
,
52
, pp.
4317
4326
.
7.
Chen
,
H. T.
,
Chen
,
P. L.
,
Horng
,
J. T.
, and
Hung
,
Y. H.
, 2005, “
Design Optimization for Pin-Fin Heat Sinks
,”
J. Electron. Packag.
,
127
, pp.
397
406
.
8.
Zitzler
,
E.
,
Laumanns
,
M.
, and
Thiele
,
L.
, 2002, “
SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization
,”
Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001)
,
Bacelona
,
Spain
, pp.
95
100
.
9.
Bandyopadhyay
,
S.
,
Saha
,
S.
,
Maulik
,
U.
, and
Deb
,
K.
, 2008,
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
,”
IEEE Trans. Evol. Comput.
,
12
, pp.
269
283
.
10.
Reyes-Sierra
,
M.
, and
Coello Coello
,
C. A.
, 2006, “
Multi-Objective Particle Swarm Optimisers: A Survey of the State-of-the-Art
,”
Int. J. Comput. Intell. Res.
,
2
, pp.
287
308
.
11.
Khan
,
W. A.
, 2006, Modeling of Fluid Flow and Heat Transfer for Optimization of Pin-Fin Heat Sinks, Ph.D. Thesis, University of Waterloo, Canada.
12.
Knowles
,
J.
, and
Corne
,
D.
, 1999, “
The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimization
,”
Proceedings of 1999 Congress on Evolutionary Computation
, Piscataway, NJ, pp.
9
105
.
13.
While
,
L.
,
Hingston
,
P.
,
Barone
,
L.
, and
Huband
,
S.
, 2006, “
A Faster Algorithm for Calculating Hypervolume
,”
IEEE Trans. Evol. Comput.
,
10
, pp.
29
38
.
14.
Tan
,
K. C.
,
Yang
,
Y. J.
, and
Goh
,
C. K.
, 2006, “
A Distributed Cooperative Coevolutionary Algorithm for Multiobjective Optimization
,”
IEEE Trans. Evol. Comput.
,
10
, pp.
527
549
.
15.
Zitzler
,
E.
,
Deb
,
K.
, and
Thiele
,
L.
, 2000,
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
,”
Evol. Comput.
,
8
, pp.
173
195
.
You do not currently have access to this content.