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Introducing Kimeme, a Novel Platform for Multi-disciplinary Multi-objective Optimization

  • Giovanni Iacca
  • Ernesto Mininno
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 587)

Abstract

Optimization processes are an essential element in many practical applications, such as in engineering, chemistry, logistic, finance, etc. To fill the knowledge gap between practitioners and optimization experts, we developed Kimeme, a new flexible platform for multi-disciplinary optimization. A peculiar feature of Kimeme is that it can be used both for problem and algorithm design. It includes a rich graphical environment, a comprehensive set of post-processing tools, and an open-source library of state-of-the-art single and multi-objective optimization algorithms. In a memetic fashion, algorithms are decomposed into operators, so that users can easily create new optimization methods, just combining built-in operators or creating new ones. Similarly, the optimization process is described according to a data-flow logic, so that it can be seamlessly integrated with external software such as Computed Aided Design & Engineering (CAD/CAE) packages, Matlab, spreadsheets, etc. Finally, Kimeme provides a native distributed computing framework, which allows parallel computations on clusters and heterogeneous LANs. Case studies from industry show that Kimeme can be effortlessly applied to industrial optimization problems, producing robust results also in comparison with other platforms on the market.

Keywords

Multi-disciplinary optimization Software design Graphical interface Distributed computing 

References

  1. 1.
  2. 2.
    Tenne, Y., Goh, C.K.: Computational Intelligence in Optimization. Springer, Heidelberg (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Koziel, S., Yang, X.S.: Computational Optimization, Methods and Algorithms, vol. 356. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  4. 4.
    Zelinka, I., Snasel, V., Abraham, A.: Handbook of Optimization: From Classical to Modern Approach, vol. 38. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  5. 5.
    Red Cedar Technology: HEEDS\(\textregistered \) MDO. http://www.redcedartech.com
  6. 6.
    Altair: HyperStudy\(\copyright \). http://www.altairhyperworks.com
  7. 7.
    Dassault Systèmes: Isight\(\copyright \). http://www.3ds.com
  8. 8.
    LIONlab: LIONsolver. http://lionoso.com/
  9. 9.
    ESTECO: modeFRONTIER\(\textregistered \). http://www.esteco.com/modefrontier
  10. 10.
    German Aerospace Center, Institute of System Dynamics and Control, AircraftSystems Dynamics: MOPS. http://www.dlr.de/rm/en/desktopdefault.aspx/tabid-3842/6343_read-9099/
  11. 11.
    iChrome: Nexus\(\copyright \). http://ichrome.com/solutions/nexus
  12. 12.
    NASA Glenn Research Center: OpenMDAO. http://openmdao.org/
  13. 13.
    Wilde Analysis Ltd.: Optimus\(\textregistered \). http://wildeanalysis.co.uk/fea/software/optimus
  14. 14.
    OptiY GmbH: OptiY\(\copyright \). http://www.optiy.eu/
  15. 15.
    FEA-Opt Technology: SmartDO\(\copyright \). http://www.smartdo.co/
  16. 16.
    Optimal Computing: Xtreme\(\copyright \). http://www.optimalcomputing.be/
  17. 17.
    Sanchez, E., Schillaci, M., Squillero, G.: Evolutionary Optimization: The \(\mu \)GP Toolkit, 1st edn. Springer Publishing Company Inc., Berlin (2011)CrossRefGoogle Scholar
  18. 18.
    Cyber Dyne Srl: Kimeme. http://cyberdynesoft.it/
  19. 19.
    Deb, K.: Multi-objective optimization. In: Burke, E.K., Kendall, C. (eds.) Search Methodologies, pp. 403–449. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  20. 20.
    Köppen, M., Schaefer, G., Abraham, A.: Intelligent Computational Optimization in Engineering, pp. 300–331. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Yang, X.S., Koziel, S.: Computational Optimization and Applications in Engineering and Industry, vol. 359. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Chen, S.H., Wang, P.P.: Computational Intelligence in Economics and Finance. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  23. 23.
    AVL: AVL™ CAMEO. https://www.avl.com/cameo
  24. 24.
    Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. J. Global Optim. 11(TR–95–012), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Beyer, H.G.: The Theory of Evolution Strategies. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar
  26. 26.
    Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar
  27. 27.
    Nelder, A., Mead, R.: A simplex method for function optimization. Comput. J. 7, 308–313 (1965)CrossRefzbMATHGoogle Scholar
  28. 28.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  29. 29.
    Coello Coello, C.A., Lechuga, M.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC 2002, vol. 2, pp. 1051–1056 (2002)Google Scholar
  30. 30.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm (2001)Google Scholar
  31. 31.
    Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)CrossRefGoogle Scholar
  32. 32.
    Iacca, G., Neri, F., Mininno, E., Ong, Y.S., Lim, M.H.: Ockham’s razor in memetic computing: three stage optimal memetic exploration. Inf. Sci. 188, 17–43 (2012)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Caraffini, F., Neri, F., Iacca, G., Mol, A.: Parallel memetic structures. Inf. Sci. 227, 60–82 (2013)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Iacca, G., Caraffini, F., Neri, F.: Memory-saving memetic computing for path-following mobile robots. Appl. Soft Comput. 13(4), 2003–2016 (2013)CrossRefGoogle Scholar
  35. 35.
    Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol. 379. Springer, Heidelberg (2011)Google Scholar
  36. 36.
    Caraffini, F., Iacca, G., Neri, F., Mininno, E.: The importance of being structured: a comparative study on multi stage memetic approaches. In: 2012 12th UK Workshop on Computational Intelligence (UKCI), pp. 1–8. IEEE (2012)Google Scholar
  37. 37.
    Mühlenbein, H.: Parallel genetic algorithms, population genetics and combinatorial optimization. In: Becker, J.D., Eisele, I., Mündemann, F.W. (eds.) Parallelism, Learning, Evolution. LNCS, vol. 565, pp. 398–406. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  38. 38.
    Cyber Dyne Srl: Kimeme Quick GuideGoogle Scholar
  39. 39.
    Jha, R., Sen, P.K., Chakraborti, N.: Multi-objective genetic algorithms and genetic programming models for minimizing input carbon rates in a blast furnace compared with a conventional analytic approach. Steel Res. Int. 85(2), 219–232 (2014)CrossRefGoogle Scholar
  40. 40.
    Pettersson, F., Chakraborti, N., Saxén, H.: A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Appl. Soft Comput. 7(1), 387–397 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Cyber Dyne S.r.l.BariItaly

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