Hardware Reference
In-Depth Information
problem and in the algorithms can be obtained combining the results of different
measurements. Another important suggestion is to check the chosen metric values
at previously defined fixed numbers of evaluations. Finally, the validation stage
can be considered concluded only when the new optimization algorithms have been
tested against other kind of approaches (classical algorithms, manual optimization
protocols, etc).
The algorithms and the procedure described in this chapter prove the overall
reliability of the Design Space exploration tools, modeFRONTIER and Multicube
Explorer in handling and in solving optimization problems in the field of Embedded
System Design. The peculiarities of such an environment have been sufficiently
recognized and exploited in order to provide solutions in affordable computational
time (considering also the high consuming simulators). The study has been enough
deep to open new questions for improving the capabilities of the algorithms in this
field as well as for opening new research directions.
References
1. Aittokoski, T., Miettinen, K.: Efficient evolutionary method to approximate the pareto opti-
mal set in multiobjective optimization.
In: Proc. International Conference on Engineering
Optimization (EngOpt) (2008)
2. Baumgartner, U., Magele, C., Renhart, W.: Pareto optimality and particle swarm optimization.
IEEE Transactions on Magnetics 40(2) , 1172-1175 (2004)
3. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting ge-
netic algorithm for multi-objective optimization: Nsga-ii. IEEE Transactions on Evolutionary
Computation 6 (2), 181-197 (2002)
4. Deb, K., Goel, T.: Controlled elitist non-dominated sorting genetic algorithm for better
convergence (2001). KanGal Report 200004
5. Erbas, C., Cerav-Erbas, S., Pimentel, A.: Multiobjective optimization and evolutionary algo-
rithms for the application mapping problem in multiprocessor system-on-chip design. IEEE
Transactions on Evolutionary Computation 10 (3), 358-374 (2006)
6. Gelatt Jr., C.D., Vecchi, M., Kirkpatrik, S.: Optimization by simulated annealing.
Science
220 (4598), 671-680 (1983)
7. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE
International Conference on Neural Networks, pp. 1942-1948 (1995)
8. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm.
In:
Proceedings of the Conference on Systems, Man and Cybernetics, pp. 4104-4109 (1997)
9. Metropolis, N., Rosenbluth, A., Teller, A., Teller, E.: Equation of state calculation by fast
computing machines. J. Chem. Phys. 21 (1953), 1087-1092 (1953)
10. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publisher (1999)
11. Poloni, C., Pedirola, V.: Ga coupled with computationally expensive simulations: Tools to im-
prove efficiency. In: Genetic Algorithms and Evolution Strategy in Engineering and Computer
Science, chap. 13. John Wiley & Sons (1998)
12. Pugh, J., Martinoli, A.: Discrete multi-valued particle swarm optimization. In: Proceedings of
IEEE Swarm Intelligence Symposium, pp. 103-110 (2006)
13. Reyes-Sierra, M., Coello, C.A.: Multiple-objective particle swarm optimizers: A survey of the
state of the art. http://www.lania.mx/
ccoello/EMOO/reyes06.pdf.gz (2006)
14. Schwefel, H.: Evolution and Optimum Seeking. Wiley & Sons (1995)
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