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Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements

Abstrakt

In this work the swarm behavior principles of Craig W. Reynolds are combined with deterministic traits. This is done by using leaders with motions based on space filling curves like Peano and Hilbert. Our goal is to evaluate how the swarm of agents works with this approach, supposing the entire swarm will better explore the entire space. Therefore, we examine different combinations of Peano and Hilbert with the already known swarm algorithms and test them in a practical challenge for the harvesting of manganese nodules on the sea ground with the use of autonomous agents. We run experiments with various settings, then evaluate and describe the results. In the last section some further development ideas and thoughts for the expansion of this study are considered.

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Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2018, Springer Nature)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Evolving Systems nr 11, strony 383 - 396,
ISSN: 1868-6478
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Logofătu D., Sobol G., Andersson C., Stamate D., Balabanov K., Cejrowski T.: Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements// Evolving Systems -Vol. 11, (2020), s.383-396
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s12530-018-9245-9
Bibliografia: test
  1. Barnsley M. F., Fractals Everywhere, Dover Books on Mathematics, New Edition, ISBN 978-0486488707 (2012) otwiera się w nowej karcie
  2. Floreano, D., Mattiussi, C.: Bio-inspired artificial intelligence : theories, methods, and technologies. MIT Press, Cambridge (2008)
  3. Fry B., Reas C., Processing, https://processing.org/ [Accessed 8-May-2018]
  4. Vlissides J., Johnson R., Helm R., Gamma E.: Design Patterns: Elements of Reusable Object- Oriented. Springer, Addison-Wesley Professional, Berlin (1994)
  5. Kennedy J., Eberhart R., Particle swarm optimization, IEEE Conference on Neural Networks, 4, 1942-1948 otwiera się w nowej karcie
  6. Kim Min Jun , Kim Jung Gu, Effect of Manganese on the Corrosion Behavior of Low Carbon Steel in 10 wt.% Sulfuric Acid, Int. J. Electrochem. Sci., 6872-6885, 10 (2015)
  7. Logof˘atu D., Sobol G., Stamate D., Particle Swarm Optimization Algorithms for Autonomous Robots with Leaders Using Hilbert Curves, 18th International Conference on Engineering Applications of Neural Networks (EANN 2017), pp. 535-543. Springer, Athen (2017) otwiera się w nowej karcie
  8. Logof˘atu D., Sobol G., Stamate D., Balabanov K., A Novel Space Filling Curves Based Approach to PSO Algorithms for Autonomous Agents, 9th International Conference on Computational Collective Intelligence (ICCCI 2017), pp. 361-370, Springer, Nicosia (2017) otwiera się w nowej karcie
  9. Muro C., Escobedo L., Spector L., Coppinger R. P., Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations, Behavioral Processes, Vol. 88, Issue 3, 192-197 (2011) otwiera się w nowej karcie
  10. Norris, J. S.: Mission-critical development with open source software: lessons learned. In: IEEE Software, pp. 42-49, 21 (January), (2004) 11. Detailed requirements for the first prototype, http://docplayer.org/22922344-Informaticup-informaticup-2014-aufgabe-manganernte-einfuehrung- 1-aufgabe.html [Accessed 8-May-2018] otwiera się w nowej karcie
  11. Reynolds W., Boids (simulated flocking), http://www.red3d.com/cwr/boids [Accessed 8May- 2018]
  12. Rodriguez F., Garcia-Martinez C., An Artificial Bee Colony Algorithm for the Unrelated Parallel Machines Scheduling Problem, PPSN XII (II), 143-152, Springer, Taormina (2012) otwiera się w nowej karcie
  13. Rossum J. R., Fundamentals of Metallic Corrosion in Fresh Water, http://www.roscoemoss.com/wp-content/uploads/publications/fmcf.pdf [Accessed 8-May-2018] otwiera się w nowej karcie
  14. Canyameres S., Logof˘atu D., Platform for Simulation and Improvement of Swarm Behavior in Changing Environments, 10 th International Conference Artificial Intelligence Applications and Innovations (AIAI 14), Springer LNCS, Island of Rhodes, Greece (2014) otwiera się w nowej karcie
  15. Shyr W.-J., Parameters Determination for Optimum Design by Evolutionary Algorithm, Convergence and Hybrid Information Technologies,, DOI: 10.5772/9638, (2010) https://www.intechopen.com/books/convergence-and-hybrid-informationtechnologies/parameters- determination-for-optimum-design-by-evolutionaryalgorithm [Accessed 8-May-2018] otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 91 razy

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