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International Workshop on Machine Learning, Optimization, and Big Data

MOD 2017: Machine Learning, Optimization, and Big Data pp 198–209Cite as

Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

Abstract

A multi-objective deterministic hybrid algorithm (MODHA) is introduced for efficient simulation-based design optimization. The global exploration capability of multi-objective deterministic particle swarm optimization (MODPSO) is combined with the local search accuracy of a derivative-free multi-objective (DFMO) linesearch method. Six MODHA formulations are discussed, based on two MODPSO formulations and three DFMO activation criteria. Forty five analytical test problems are solved, with two/three objectives and one to twelve variables. The performance is evaluated by two multi-objective metrics. The most promising formulations are finally applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions and compared to MODPSO and DFMO, showing promising results.

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References

  1. Qian, C., Yu, Y., Zhou, Z.H.: Pareto ensemble pruning. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 2935–2941. AAAI Press (2015)

    Google Scholar 

  2. Qian, C., Tang, K., Zhou, Z.-H.: Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 835–846. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_78

    Chapter  Google Scholar 

  3. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  4. Serani, A., Leotardi, C., Iemma, U., Campana, E.F., Fasano, G., Diez, M.: Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Appl. Soft Comput. 49, 313–334 (2016)

    Article  Google Scholar 

  5. Serani, A., Diez, M., Campana, E.F., Fasano, G., Peri, D., Iemma, U.: Globally convergent hybridization of particle swarm optimization using line search-based derivative-free techniques. In: Yang, X.S. (ed.) Recent Advances in Swarm Intelligence and Evolutionary Computation. SCI, vol. 585, pp. 25–47. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13826-8_2

    Chapter  Google Scholar 

  6. Serani, A., Fasano, G., Liuzzi, G., Lucidi, S., Iemma, U., Campana, E.F., Stern, F., Diez, M.: Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms. Appl. Ocean Res. 59, 115–128 (2016)

    Article  Google Scholar 

  7. Liu, D., Tan, K.C., Goh, C.K., Ho, W.K.: A multiobjective memetic algorithm based on particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(1), 42–50 (2007)

    Article  Google Scholar 

  8. Kaveh, A., Laknejadi, K.: A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization. Expert Syst. Appl. 38(12), 15475–15488 (2011)

    Article  Google Scholar 

  9. Cheng, S., Zhan, H., Shu, Z.: An innovative hybrid multi-objective particle swarm optimization with or without constraints handling. Appl. Soft Comput. 47, 370–388 (2016)

    Article  Google Scholar 

  10. Santana-Quintero, L.V., Ramírez, N., Coello, C.C.: A multi-objective particle swarm optimizer hybridized with scatter search. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 294–304. Springer, Heidelberg (2006). https://doi.org/10.1007/11925231_28

    Chapter  Google Scholar 

  11. Izui, K., Nishiwaki, S., Yoshimura, M., Nakamura, M., Renaud, J.E.: Enhanced multiobjective particle swarm optimization in combination with adaptive weighted gradient-based searching. Eng. Optim. 40(9), 789–804 (2008)

    Article  MathSciNet  Google Scholar 

  12. Mousa, A., El-Shorbagy, M., Abd-El-Wahed, W.: Local search based hybrid particle swarm optimization algorithm for multiobjective optimization. Swarm Evol. Comput. 3, 1–14 (2012)

    Article  Google Scholar 

  13. Xu, G., Yang, Y.Q., Liu, B.B., Xu, Y.H., Wu, A.J.: An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search. J. Comput. Appl. Math. 280, 310–326 (2015)

    Article  MathSciNet  Google Scholar 

  14. Pellegrini, R., Serani, A., Leotardi, C., Iemma, U., Campana, E.F., Diez, M.: Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization. Appl. Soft Comput. 58, 714–731 (2017)

    Article  Google Scholar 

  15. Liuzzi, G., Lucidi, S., Rinaldi, F.: A derivative-free approach to constrained multiobjective nonsmooth optimization. SIAM J. Optim. 26(4), 2744–2774 (2016)

    Article  MathSciNet  Google Scholar 

  16. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056872

    Chapter  Google Scholar 

  17. Diez, M., Campana, E.F., Stern, F.: Development and evaluation of hull-form stochastic optimization methods for resistance and operability. In: Proceedings of the 13th International Conference on Fast Sea Transportation (FAST 2015) (2015)

    Google Scholar 

  18. Czyzak, P., Jaszkiewicz, A.: Pareto simulated annealing-a metaheuristic technique for multiple-objective combinatorial optimization. J. Multi-criteria Decis. Anal. 7(1), 34–47 (1998)

    Article  Google Scholar 

  19. Jiang, S., Ong, Y.S., Zhang, J., Feng, L.: Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Trans. Cybern. 44(12), 2391–2404 (2014)

    Article  Google Scholar 

  20. Campana, E.F., Diez, M., Iemma, U., Liuzzi, G., Lucidi, S., Rinaldi, F., Serani, A.: Derivative-free global ship design optimization using global/local hybridization of the DIRECT algorithm. Optim. Eng. 17(1), 127–156 (2015)

    Article  MathSciNet  Google Scholar 

  21. Pinto, A., Peri, D., Campana, E.F.: Multiobjective optimization of a containership using deterministic particle swarm optimization. J. Ship Res. 51(3), 217–228 (2007)

    Google Scholar 

  22. Wong, T., Luk, W., Heng, P.: Sampling with Hammersley and Halton points. J. Graphics Tools 2(2), 9–24 (1997)

    Article  Google Scholar 

  23. Clerc, M.: Stagnation analysis in particle swarm optimization or what happens when nothing happens (2006). http://clerc.maurice.free.fr/pso

  24. Fonseca, C.M., Paquete, L., Lòpez-Ibàñez, M.: An improved dimension - sweep algorithm for the hypervolume indicator. In: Proceedings of the Congress on Evolutionary Computation (CEC 2006), pp. 1157–1163. IEEE (2006)

    Google Scholar 

  25. Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transa. Evol. Comput. 10(5), 477–506 (2006)

    Article  Google Scholar 

  26. Volpi, S., Diez, M., Gaul, N., Song, H., Iemma, U., Choi, K.K., Campana, E.F., Stern, F.: Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification. Struct. Multidisciplinary Optim. 51(2), 347–368 (2015)

    Article  Google Scholar 

  27. Raquel, C.R., Naval Jr., P.C.: An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM (2005)

    Google Scholar 

  28. Žilinskas, A.: Visualization of a statistical approximation of the pareto front. Appl. Math. Comput. 271, 694–700 (2015)

    MathSciNet  Google Scholar 

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Acknowledgements

The work is supported by the US Office of Naval Research Global, NICOP grant N62909-15-1-2016, under the administration of Dr. Woei-Min Lin, Dr. Salahuddin Ahmed, and Dr. Ki-Han Kim, and by the Italian Flagship Project RITMARE, founded by the Italian Ministry of Education.

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Authors and Affiliations

  1. CNR-INSEAN, National Research Council, Marine Technology Research Institute, Rome, Italy

    Riccardo Pellegrini, Andrea Serani, Emilio F. Campana & Matteo Diez

  2. CNR-IASI, National Research Council, Institute for Systems Analysis and Computer Science, Rome, Italy

    Giampaolo Liuzzi

  3. Department of Mathematics, University of Padua, Padua, Italy

    Francesco Rinaldi

  4. Department of Computer, Control and Management Engineering “A. Ruberti”, Sapienza University, Rome, Italy

    Stefano Lucidi

  5. Department of Engineering, Roma Tre University, Rome, Italy

    Umberto Iemma

Authors
  1. Riccardo Pellegrini
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  2. Andrea Serani
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  3. Giampaolo Liuzzi
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  4. Francesco Rinaldi
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  5. Stefano Lucidi
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  6. Emilio F. Campana
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  7. Umberto Iemma
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  8. Matteo Diez
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Corresponding author

Correspondence to Matteo Diez .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Pellegrini, R. et al. (2018). Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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