Have a personal or library account? Click to login
An Adaptive Island Model of Population for Neuroevolutionary Ship Handling Cover

An Adaptive Island Model of Population for Neuroevolutionary Ship Handling

Open Access
|Jan 2022

References

  1. 1. D. Whitley, S. Rana, and R. Heckendorn, “The Island Model Genetic Algorithm: On Separability, Population Size and Convergence,” Journal of Computing and Information Technology, vol. 7, 1998.
  2. 2. H. M. Pandey, A. Chaudhary, and D. Mehrotra, “A comparative review of approaches to prevent premature convergence in GA,” Applied Soft Computing, vol. 24, pp. 1047–1077, 2014, doi: <a href="https://doi.org/10.1016/j.asoc.2014.08.025.10.1016/j.asoc.2014.08.025" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.asoc.2014.08.025.10.1016/j.asoc.2014.08.025</a>
  3. 3. E. Alba and J. M. Troya, “An analysis of synchronous and asynchronous parallel distributed genetic algorithms with structured and panmictic Islands,” in Parallel and Distributed Processing, Berlin, Heidelberg, 1999, pp. 248–256.<a href="https://doi.org/10.1007/BFb0097906" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/BFb0097906</a>
  4. 4. A. Hoang et al., “A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels,” Sustainable Energy Technologies and Assessments, Jun. 2021, doi: <a href="https://doi.org/10.1016/j.seta.2021.101416.10.1016/j.seta.2021.101416" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.seta.2021.101416.10.1016/j.seta.2021.101416</a>
  5. 5. S. L. Boung Yew and K. K. Kee, “Artificial Neural Network Back-Propagation Based Decision Support System for Ship Fuel Consumption Prediction,” 2018. doi: <a href="https://doi.org/10.1049/cp.2018.1306.10.1049/cp.2018.1306" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1049/cp.2018.1306.10.1049/cp.2018.1306</a>
  6. 6. W. Tarełko and K. Rudzki, “Applying artificial neural networks for modelling ship speed and fuel consumption,” Neural Computing & Applications, vol. 32, pp. 17379–17395, 2020. doi: <a href="https://doi.org/10.1007/s00521-020-05111-210.1007/s00521-020-05111-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s00521-020-05111-210.1007/s00521-020-05111-2</a>
  7. 7. J. Liu, G. Shi, and K. Zhu, “Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR),” Applied Sciences, vol. 9, p. 2983, 2019, doi: <a href="https://doi.org/10.3390/app9152983.10.3390/app9152983" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/app9152983.10.3390/app9152983</a>
  8. 8. K. Bobkowska and I. Bodus-Olkowska Izabela, “Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification,” Polish Maritime Research, vol. 27, pp. 170–178, 2020. doi: <a href="https://doi.org/10.2478/pomr-2020-007710.2478/pomr-2020-0077" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/pomr-2020-007710.2478/pomr-2020-0077</a>
  9. 9. G. Li, B. Kawan, H. Wang, and H. Zhang, “Neural-network-based modelling and analysis for time series prediction of ship motion,” Ship Technology Research, vol. 64, 2017, doi: <a href="https://doi.org/10.1080/09377255.2017.1309786.10.1080/09377255.2017.1309786" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/09377255.2017.1309786.10.1080/09377255.2017.1309786</a>
  10. 10. T. Niksa-Rynkiewicz and A. Witkowska, “Analysis of impact of ship model parameters on changes of control quality index in ship dynamic positioning system,” Polish Maritime Research, vol. 26, no. 1(101), pp. 6–14, 2019. doi: <a href="https://doi.org/10.2478/pomr-2019-000110.2478/pomr-2019-0001" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/pomr-2019-000110.2478/pomr-2019-0001</a>
  11. 11. J. Lisowski, “Computational Intelligence in Marine Control Engineering Education,” Polish Maritime Research, vol. 28, no. 1, pp. 163–172, 2021, doi:<a href="https://doi.org/10.2478/pomr-2021-0015.10.2478/pomr-2021-0015" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/pomr-2021-0015.10.2478/pomr-2021-0015</a>
  12. 12. R. Lopes, R. Pedrosa Silva, F. Campelo, and F. Guimarães, “A Multi-agent Approach to the Adaptation of Migration Topology in Island Model Evolutionary Algorithms,” in Proceedings - Brazilian Symposium on Neural Networks, SBRN, 2012, pp. 160–165. doi: <a href="https://doi.org/10.1109/SBRN.2012.36.10.1109/SBRN.2012.36" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/SBRN.2012.36.10.1109/SBRN.2012.36</a>
  13. 13. P. García-Sánchez, J. Ortega, J. González, P. A. Castillo, and J. J. Merelo, “Distributed multi-objective evolutionary optimization using island-based selective operator application,” Applied Soft Computing, vol. 85, p. 105757, 2019, doi: <a href="https://doi.org/10.1016/j.asoc.2019.105757.10.1016/j.asoc.2019.105757" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.asoc.2019.105757.10.1016/j.asoc.2019.105757</a>
  14. 14. E. Cantú-Paz and D. E. Goldberg, “Are Multiple Runs of Genetic Algorithms Better than One?,” in Genetic and Evolutionary Computation — GECCO 2003, Berlin, Heidelberg, 2003, pp. 801–812.<a href="https://doi.org/10.1007/3-540-45105-6_94" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/3-540-45105-6_94</a>
  15. 15. R. Śmierzchalski, Ł. Kuczkowski, P. Kolendo, and B. Jaworski, “Distributed Evolutionary Algorithm for Path Planning in Navigation Situation,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 7, pp. 293–300, 2013, doi: <a href="https://doi.org/10.12716/1001.07.02.17.10.12716/1001.07.02.17" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.12716/1001.07.02.17.10.12716/1001.07.02.17</a>
  16. 16. A. Skakovski and P. Jędrzejowicz, “An island-based differential evolution algorithm with the multi-size populations,” Expert Systems with Applications, vol. 126, pp. 308–320, 2019, doi: <a href="https://doi.org/10.1016/j.eswa.2019.02.027.10.1016/j.eswa.2019.02.027" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.eswa.2019.02.027.10.1016/j.eswa.2019.02.027</a>
  17. 17. J. Szlapczynska and R. Szlapczynski, “Preference-based evolutionary multi-objective optimization in ship weather routing,” Applied Soft Computing, vol. 84, p. 105742, 2019, doi: <a href="https://doi.org/10.1016/j.asoc.2019.105742.10.1016/j.asoc.2019.105742" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.asoc.2019.105742.10.1016/j.asoc.2019.105742</a>
  18. 18. L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement Learning: A Survey,” Journal of Artificial Intelligence Research, vol. cs.AI/9605, pp. 237–285, 1996, doi: <a href="https://doi.org/10.1613/jair.301.10.1613/jair.301" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1613/jair.301.10.1613/jair.301</a>
  19. 19. R. Maeda and M. Mimura, “Automating post-exploitation with deep reinforcement learning,” Computers & Security, vol. 100, p. 102108, 2021, doi: <a href="https://doi.org/10.1016/j.cose.2020.102108.10.1016/j.cose.2020.102108" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.cose.2020.102108.10.1016/j.cose.2020.102108</a>
  20. 20. R. De Nardi, J. Togelius, O. E. Holland, and S. M. Lucas, “Evolution of Neural Networks for Helicopter Control: Why Modularity Matters,” Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pp. 1799–1806, 2006, doi: citeulike-article-id:4142097.
  21. 21. N. T. Siebel and G. Sommer, “Evolutionary reinforcement learning of artificial neural networks,” International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems, vol. 4, pp. 171–183, 2007.<a href="https://doi.org/10.3233/HIS-2007-4304" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3233/HIS-2007-4304</a>
  22. 22. K. O. Stanley and M. Risto, “Efficient Reinforcement Learning Through Evolving Neural Network Topologies,” presented at the Proceedings of the Genetic and Evolutionary Computation Conference, 2002.
  23. 23. T. I. Fossen, Guidance and control of ocean vehicles. Chichester, UK: Wiley, 1994.
  24. 24. R. Zaccone, M. Martelli, and M. Figari, “A COLREG-Compliant Ship Collision Avoidance Algorithm,” Jun. 2019, pp. 2530–2535. doi: <a href="https://doi.org/10.23919/ECC.2019.8796207.10.23919/ECC.2019.8796207" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.23919/ECC.2019.8796207.10.23919/ECC.2019.8796207</a>
DOI: https://doi.org/10.2478/pomr-2021-0056 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
Language: English
Page range: 142 - 150
Published on: Jan 1, 2022
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2022 Mirosław Łącki, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.