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A Multi-tiered Memetic Multiobjective Evolutionary Algorithm for the Design of Quantum Cascade Lasers

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Evolutionary Multi-Criterion Optimization (EMO 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

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Abstract

Recent advances in quantum cascade lasers (QCLs) have enabled their use as (tunable) emission sources for chemical and biological spectroscopy, as well as allowed their demonstration in applications in medical diagnostics and potential homeland security systems. Finding the optimal design solution can be challenging, especially for lasers that operate in the terahertz region. The production process is prohibitive, so an optimization algorithm is needed to find high quality QCL designs. Past research attempts using multiobjective evolutionary algorithms (MOEAs) have found good solutions, but lacked a local search element that could enable them to find more effective solutions. This research looks at two memetic MOEAs that use a neighborhood search. Our baseline memetic MOEA used a simple neighborhood search, which is similar to other MOEA neighborhood searches found in the literature. Alternatively, our innovative multi-tiered memetic MOEA uses problem domain knowledge to change the temporal focus of the neighborhood search based on the generation. It is empirically shown that the multi-tiered memetic MOEA is able to find solutions that dominate the baseline memetic algorithm. Additional experiments suggest that using local search on only non-dominated individuals improves the effectiveness and efficiency of the algorithm versus applying the local search to dominated individuals as well. This research validates the importance of using multiobjective problem (MOP) domain knowledge in order to obtain the best results for a real world solution. It also introduces a new multi-tiered local search procedure that is able to focus the local search on specific critical elements of the problem at different stages in the optimization process.

The views expressed in this article are those of the authors and do not reflect the official policy of the United States Air Force, Department of Defense, or the United States Government.

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References

  1. Capasso, F., Gmachl, C., Sivco, D.L., Cho, A.Y.: Quantum cascade lasers. Physics Today (2002)

    Google Scholar 

  2. Tihov, M.I.: Chemical sensors based on distributed feedback quantum cascade laser for environmental monitoring. Master’s thesis, Ecole Polytechnique (2003)

    Google Scholar 

  3. Rodríguez, A.F., Keller, T.A., Lamont, G.B., Nelson, T.R.: Using a Multiobjective Evolutionary Algorithm to Develop a Quantum Cascade Laser Operating in the Terahertz Frequency Range. In: 2005 IEEE Congress on Evolutionary Computation (CEC 2005), vol. 1, Edinburgh, Scotland, September 2005, pp. 9–16. IEEE Computer Society Press, Los Alamitos (2005)

    Chapter  Google Scholar 

  4. Keller, T.A., Lamont, G.B.: Optimization of a Quantum Cascade Laser Operating in the Terahertz Frequency Range Using a Multiobjective Evolutionary Algorithm. In: 17th International Conference on Multiple Criteria Decision Making (MCDM 2004), vol. 1, December (2004)

    Google Scholar 

  5. Capasso, F., Faist, J., L., S.D., Sirtori, C., L., H.A., Chu, S.N.G., Cho, A.Y.: Quantum cascade laser: A unipolar intersubband semiconductor laser. In: 14th IEEE International Semiconductor Laser Conference, pp. 71–72. IEEE Computer Society Press, Los Alamitos (1994)

    Chapter  Google Scholar 

  6. Capasso, F., Sivco, D.L., Cho, A.Y., Gmachl, C.: Quantum cascade lasers. Physics World 12, 27 (1999)

    Google Scholar 

  7. Ouellette, J.: Quantum cascade lasers turn commercial. The Industrial Physicist 7(2), 8–13 (2001)

    Google Scholar 

  8. Menon, V.: Design, fabrication and characterization of quantum cascade terahertz emitters, PhD Dissertation (2001)

    Google Scholar 

  9. Ram-Mohan, L.R.: Quantum Cascade Terahertz Lasers. Private Communications, Air Force Research Labs, Wright Patterson AFB, OH (2003)

    Google Scholar 

  10. Banerjee, S., S., S.P., A., S.K.: Design of a tunable quantum cascade laser with enhanced optical non-linearities. In: IEEE Proceddings on Optoelectronics, vol. 153, pp. 40–42. IEEE, Los Alamitos (2006)

    Google Scholar 

  11. Friedrich, A., Boehm, G., Amann, M.-C.: Low-threshold quantum-cascade lasers without injector regions, emitting at /spl lambda/ /spl sim/ 6.7 /spl mu/m. In: Proceddings of the 2006 International Conference on Indium Phosphide and Related Materials, pp. 19–22 (2006)

    Google Scholar 

  12. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)

    MATH  Google Scholar 

  13. Julstrom, B.A.: Comparing Darwinian, Baldwinian, and Lamarckian search in a genetic algorithm for the 4-cycle problem. In: Brave, S., Wu, A.S. (eds.) Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, 13 July 1999, pp. 134–138 (1999)

    Google Scholar 

  14. Ishibuchi, H., Kaige, S., Narukawa, K.: Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 370–385. Springer, Heidelberg (2005)

    Google Scholar 

  15. Ku, K.W.C., Mak, M.W.: Exploring the effects of lamarckian and baldwinian learning in evolving recurrent neural networks. In: Fogel, D.B. (ed.) Proceedings of the Fourth IEEE Conference on Evolutionary Computation (ICEC’97), Piscataway, New Jersey, pp. 617–621. IEEE Computer Society Press, Los Alamitos (1995)

    Google Scholar 

  16. Gruau, F., Whitley, L.D.: Adding learning to the cellular development of neural networks: Evolution and the baldwin effect. Evolutionary Computation 1(3), 213–233 (1993)

    Article  Google Scholar 

  17. Goel, T., Deb, K.: Hybrid Methods for Multi-Objective Evolutionary Algorithms. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02), vol. 1, Singapore, November 2002, pp. 188–192 (2002)

    Google Scholar 

  18. Knowles, J., Corne, D.: M-PAES: A Memetic Algorithm for Multiobjective Optimization. In: 2000 Congress on Evolutionary Computation, vol. 1, Piscataway, New Jersey, July 2000, pp. 325–332. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

  19. Ishibuchi, H., Narukawa, K.: Performance evaluation of simple multiobjective genetic local search algorithms on multiobjective 0/1 knapsack problems. In: 2004 Congress on Evolutionary Computation (CEC’2004), vol. 1, Portland, Oregon, USA, June 2004, pp. 441–448. IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  20. Thomson, R., Arslan, T.: The evolutionary design and synthesis of non-linear digital vlsi systems. In: EH ’03: Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware, Washington, DC, USA, pp. 125–134 (2003)

    Google Scholar 

  21. Jin, Y., Okabe, T., Sendhoff, B.: Adapting Weighted Aggregation for Multiobjective Evolution Strategies. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 96–110. Springer, Heidelberg (2001)

    Google Scholar 

  22. Leiva, H.A., Esquivel, S.C., Gallard, R.H.: Multiplicity and local search in evolutionary algorithms to build the pareto front. In: SCCC ’00: Proceedings of the XX International Conference of the Chilean Computer Science Society (SCCC’00), Washington, DC, USA, p. 7. IEEE Computer Society Press, Los Alamitos (2000)

    Chapter  Google Scholar 

  23. Ishibuchi, H., Murata, T.: Multi-Objective Genetic Local Search Algorithm. In: Fukuda, T., Furuhashi, T. (eds.) Proceedings of the 1996 International Conference on Evolutionary Computation, Nagoya, Japan, pp. 119–124. IEEE Computer Society Press, Los Alamitos (1996)

    Chapter  Google Scholar 

  24. Deb, K., Goel, T.: A Hybrid Multi-Objective Evolutionary Approach to Engineering Shape Design. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 385–399. Springer, Heidelberg (2001)

    Google Scholar 

  25. Murata, T., Nozawa, H., Tsujimura, Y., Gen, M., Ishibuchi, H.: Effect of Local Search on the Performance of Cellular Multi-Objective Genetic Algorithms for Designing Fuzzy Rule-based Classification Systems. In: Congress on Evolutionary Computation (CEC’2002), vol. 1, May 2002, pp. 663–668 (2002)

    Google Scholar 

  26. Sato, H., Aguirre, H.E., Tanaka, K.: Local Dominance Using Polar Coordinates to Enhance Multiobjective Evolutionary Algorithms. In: 2004 Congress on Evolutionary Computation (CEC’2004), vol. 1, Portland, Oregon, June 2004, pp. 188–195 (2004)

    Google Scholar 

  27. Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India (2000)

    Google Scholar 

  28. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100 (2002)

    Google Scholar 

  29. Michalewicz, Z.: Evolutionary computation techniques for nonlinear programming problems. International Transactions in Operational Research 1(2), 175 (1994), doi:10.1145/272682.272711

    Article  Google Scholar 

  30. Knarr, M.R., Goltz, M.N., Lamont, G.B., Huang, J.: In Situ Bioremediation of Perchlorate-Contaminated Groundwater using a Multi-Objective Parallel Evolutionary Algorithm. In: Congress on Evolutionary Computation (CEC’2003), vol. 1, Piscataway, New Jersey, December 2003, pp. 1604–1611. IEEE Computer Society Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  31. Kleeman, M.P., Lamont, G.B.: Solving the aircraft engine maintenance scheduling problem using a multi-objective evolutionary algorithm. In: EMO, pp. 782–796 (2005)

    Google Scholar 

  32. Fleming, P.J., Fonseca, C.M.: Genetic algorihtms for multi-objective optimization: Formulation, discussion and generalization. In: 5th International Congress on Genetic Algorithms (ICGA), pp. 416–423 (1993)

    Google Scholar 

  33. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algoirthm for multiobjective optimization, pp. 82–87 (1994)

    Google Scholar 

  34. Michalewicz, Z., Janikow, C.Z.: Genocop: a genetic algorithm for numerical optimization problems with linear constraints. Commun. ACM 39(12es), 223–240 (1996)

    Article  Google Scholar 

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Authors and Affiliations

  1. Air Force Institute of Technology, Dept of Electrical and Computer Engineering, Graduate School of Engineering & Management, Wright-Patterson AFB (Dayton) OH, 45433, USA

    Mark P. Kleeman & Gary B. Lamont

  2. Air Force Research Laboratory, Materials and Manufacturing Directorate, Wright-Patterson AFB (Dayton) OH, 45433, USA

    Adam Cooney

  3. Air Force Research Laboratory, Sensors Directorate, Wright-Patterson AFB (Dayton) OH, 45433, USA

    Thomas R. Nelson

Authors
  1. Mark P. Kleeman
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  2. Gary B. Lamont
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  3. Adam Cooney
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  4. Thomas R. Nelson
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Editor information

Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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Kleeman, M.P., Lamont, G.B., Cooney, A., Nelson, T.R. (2007). A Multi-tiered Memetic Multiobjective Evolutionary Algorithm for the Design of Quantum Cascade Lasers. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_17

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

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