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mspMEA: The Microcones Separation Parallel Multiobjective Evolutionary Algorithm and Its Application to Fuzzy Rule-Based Ship Classification

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Recent Advances in Computational Intelligence in Defense and Security

Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

This chapter presents a new parallel multiobjective evolutionary algorithm, based on the island model, where the objective space is exploited to distribute the individuals among the processors. The algorithm, which generalizes the well-known cone separation method, mitigates most of its drawbacks. The new algorithm has been employed to speed-up the optimization of fuzzy rule-based classifiers. The fuzzy classifiers are used to build an emulator of the Ship Classification Unit (SCU) contained in modern influence mines. Having an accurate emulator of a mine’s SCU is helpful when needing: (i) to accurately evaluate the risk of traversal of a mined region by vessels/AUVs, (ii) to assess the improvements of ship signature balancing processes, and (iii) to support in-vehicle decision making in autonomous unmanned mine disposal.

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References

  1. Antonelli, M., Ducange, P., Marcelloni, F.: A new approach to handle high dimensional and large datasets in multi-objective evolutionary fuzzy systems. In: Proceedings of the 2011 IEEE International Conference on Fuzzy Systems, pp. 1286–1293, 27–30 June 2011

    Google Scholar 

  2. Branke, J., Schmeck, H., Deb, K., Reddy, S.M.: Parallelizing multi-objective evolutionary algorithms: cone separation. In: Proceedings of the 2004 Congress on Evolutionary Computation, vol. 2, pp. 19–23 (2004)

    Google Scholar 

  3. Cococcioni, M., Corsini, G., Lazzerini, B., Marcelloni, F.: Solving the ocean color inverse problem by using evolutionary multi-objective optimization of neuro-fuzzy systems. Int. J. Knowl. Based Intell. Eng. Syst. (KES) 12(5–6), 339–355 (2008)

    Google Scholar 

  4. Cococcioni, M., D’Andrea, E., Lazzerini, B.: Providing PRTools with fuzzy rule-based classifiers. In: Proceedings of the 2010 IEEE International Conference on Fuzzy Systems, pp. 1–8 (2010)

    Google Scholar 

  5. Cococcioni, M., Ducange, P., Lazzerini, B., Marcelloni, F.: A new multi-objective evolutionary algorithm based on convex hull for binary classifier optimization. In: Proceedings 2007 IEEE Congress on Evolutionary Computation (IEEE-CEC 2007), pp. 3150–3156. Singapore, 25–28 Sept 2007

    Google Scholar 

  6. Cococcioni, M., Grasso, R., Rixen, M.: A hybrid continuity preserving inference strategy to speed up Takagi-Sugeno multiobjective genetic fuzzy systems. In: Proceedings of the 5th IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS 2011), pp. 66–72. Paris, 11–15 Apr 2011

    Google Scholar 

  7. Cococcioni, M., Grasso, R., Rixen, M.: Rapid prototyping of high performance fuzzy computing applications using high level GPU programming for maritime operations support. In: Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2011), pp. 17–23. Paris, 11–15 Apr 2011

    Google Scholar 

  8. Cococcioni, M., Lazzerini, B., Marcelloni, F.: Fast multiobjective genetic rule learning using an efficient method for Takagi-Sugeno fuzzy systems identification. In: Proceedings of 8th International Conference on Hybrid Intelligent Systems, pp. 272–277. Barcelona, Spain (2008)

    Google Scholar 

  9. Cococcioni, M., Lazzerini, B., Marcelloni, F.: Towards efficient Takagi-Sugeno multi-objective genetic fuzzy systems for high dimensional problems. In: Tenne, Y., Goh, C.-K. (eds.) Computational Intelligence in Expensive Optimization Problems. Studies in Evolutionary Learning and Optimization, pp. 397–422 (2009)

    Google Scholar 

  10. Cococcioni, M., Lazzerini, B., Marcelloni, F.: On reducing computational overhead in multi-objective genetic takagi-sugeno fuzzy systems. Appl. Soft Comput. 11(1), 675–688 (2011)

    Article  Google Scholar 

  11. Cococcioni, M.: The evolutionary multiobjective optimization of fuzzy rule-based systems bibliography page. http://www.iet.unipi.it/m.cococcioni/emofrbss.html

  12. Connors, W.A., Fox, W.L.J.: Performance-based planning and evaluation for modern naval mine countermeasures systems. NATO STO. Technical report (CMRE-FR-2014-025)

    Google Scholar 

  13. de Jong, A., Cococcioni, M.: NATO fuzzy logic generic mine model. In: Proceedings of Undersea Defence Technology Europe (UDT-EUROPE 2007), pp. 1–15. Naples, Italy, 5–7 June 2007

    Google Scholar 

  14. De Jong, C.A., Quesson, B.A., Ainslie, M.A., Vermeulen, R.C. Measuring ship acoustic signatures against mine threat. In: Proceedings of Meetings on Acoustics, vol. 17, p. 070059 (2012)

    Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Dorronsoro, B., Danoy, G., Nebro, A.J., Bouvry, P.: Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution. Comput. Oper. Res. 40(6), 1552–1563 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Fazzolari, M., Giglio, B., Alcalá, R., Marcelloni, R., Herrera, H.: A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: accuracy-complexity trade-off. Knowl. Based Syst. 54, 32–41 (2013)

    Article  Google Scholar 

  18. Grasso, R., Cecchi, D., Cococcioni, M., Trees, C., Rixen, M., Alvarez, A., Strode, C.: Model based decision support for underwater glider operation monitoring. In: Proceedings of the 2010 Oceans MTS/IEEE Seattle Conference (OCEANS/Seattle 2010), pp. 1–8. Seattle, Washington, USA, 20–23 Sept 2010

    Google Scholar 

  19. Knowles, J., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)

    Article  Google Scholar 

  20. Lee, D.S., Morillo, C., Bugeda, G., Oller, S., Onate, E.: Multilayered composite structure design optimisation using distributed/parallel multi-objective evolutionary algorithms. Compos. Struct. 94(3), 1087–1096 (2012)

    Article  Google Scholar 

  21. McIntosh, D.J.: Real variability in ship systems’ noise and vibration. Design and through-life management implications for underwater noise and habitability. In: Proceedings of the INTER-NOISE and NOISE-CON Congress and Conference. Institute of Noise Control Engineering, vol. 249, issue 1, pp. 6298–6307 (2014)

    Google Scholar 

  22. Nielsen, P.L., Fox, W.L.J.: Seabed characterization for mine hunting sonar performance and mine burial predictions. NATO STO Technical report (CMRE-FR-2013-002) (2013)

    Google Scholar 

  23. Percival, A.M., Couillard, M., Midtgaard, Ø., Fox, W.L.J.: Unmanned systems, autonomy, and side-looking sonar: a framework for integrating contemporary systems into the operational MCM architecture. NATO STO CMRE Technical report (CMRE-FR-2013-013) (2013)

    Google Scholar 

  24. Song, K., Chu, P.C.: Conceptual design of future undersea unmanned vehicle (UUV) system for mine disposal. IEEE Syst. J. 8(1), 43–51 (2014)

    Article  Google Scholar 

  25. Tiwari, S., Fadel, G., Deb, K.: AMGA2: Improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization. Eng. Optim. 43(4), 377–401 (2011)

    Article  Google Scholar 

  26. Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 144–173 (2003)

    Article  Google Scholar 

  27. Zhang, Q., Hui, L.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  MathSciNet  Google Scholar 

  28. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. Technical report 103, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland (2001)

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino, 1, 56122, Pisa, Italy

    Marco Cococcioni

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  1. Marco Cococcioni
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Correspondence to Marco Cococcioni .

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Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

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Cococcioni, M. (2016). mspMEA: The Microcones Separation Parallel Multiobjective Evolutionary Algorithm and Its Application to Fuzzy Rule-Based Ship Classification. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-26450-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26448-6

  • Online ISBN: 978-3-319-26450-9

  • eBook Packages: EngineeringEngineering (R0)

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