Underwater Glider Path Planning and Population Size Reduction in Differential Evolution

详细信息    查看全文

• 关键词：Differential evolution ; Population size reduction ; Glider path planning ; Underwater robotics ; Autonomous underwater vehicle
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9520
• 期：1
• 页码：853-860
• 全文大小：550 KB
• 参考文献：1.Alvarez, A., Caiti, A., Onken, R.: Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J. Oceanic Eng. 29(2), 418–429 (2004)CrossRef
  2.Bošković, B., Brest, J., Zamuda, A., Greiner, S., Žumer, V.: History mechanism supported differential evolution for chess evaluation function tuning. Soft Comput. Fusion Found. Method. Appl. 15(4), 667–682 (2011)
  3.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)CrossRef
  4.Brest, J., Korošec, P., Šilc, J., Zamuda, A., Bošković, B., Maučec, M.S.: Differential evolution and differential ant-stigmergy on dynamic optimisation problems. Int. J. Syst. Sci. 44(4), 663–679 (2013)MathSciNet CrossRef MATH
  5.Brest, J., Maučec, M.S.: Population size reduction for the differential evolution algorithm. Appl. Intell. 29(3), 228–247 (2008)CrossRef
  6. Cabrera-Gámez, J., Isern-González, J., Hernández-Sosa, D., Domínguez-Brito, A.C., Fernández-Perdomo, E.: Optimization-Based Weather Routing for Sailboats. In: Sauze, C., Finnis, J. (eds.) Robotic Sailing 2012, pp. 23–34. Springer, Heidelberg (2013) CrossRef
  7.Darwin, C.: On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. John Murray, London (1859)CrossRef
  8.Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRef
  9.Davis, R.E., Leonard, N.E., Fratantoni, D.M.: Routing strategies for underwater gliders. Deep Sea Res. Part II 56(3), 173–187 (2009)CrossRef
  10. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Heidelberg (2003) CrossRef MATH
  11.Garau, B., Alvarez, A., Oliver, G.: Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an A* approach. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 194–198. IEEE (2005)
  12.Hátún, H., Eriksen, C.C., Rhines, P.B.: Buoyant eddies entering the Labrador Sea observed with gliders and altimetry. J. Phys. Oceanogr. 37(12), 2838–2854 (2007)CrossRef
  13. Hernández Sosa, D.J., Smith, R., Fernández-Perdomo, E., Isern-González, J., Cabrera, J., Domínguez-Brito, A.C., Prieto-Marañón, V.: Glider path-planning for optimal sampling of mesoscale eddies. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2013, Part II. LNCS, vol. 8112, pp. 321–325. Springer, Heidelberg (2013) CrossRef
  14.Inanc, T., Shadden, S.C., Marsden, J.E.: Optimal trajectory generation in ocean flows. In: Proceedings of the American Control Conference, Portland, OR, USA, pp. 674–679 (2004)
  15.Joshi, R., Sanderson, A.: Minimal representation multisensor fusion using differential evolution. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 29(1), 1083–4427 (1999)CrossRef
  16.Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998)MathSciNet CrossRef MATH
  17.Leonard, N.E., Paley, D.A., Davis, R.E., Fratantoni, D.M., Lekien, F., Zhang, F.: Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay. J. Field Rob. 27(6), 718–740 (2010)CrossRef
  18.Moura, A., Rijo, R., Silva, P., Crespo, S.: A multi-objective genetic algorithm applied to autonomous underwater vehicles for sewage outfall plume dispersion observations. Appl. Soft Comput. 10(4), 1119–1126 (2010)CrossRef
  19. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005) MATH
  20.Rudnick, D.L., Davis, R.E., Eriksen, C.C., Fratantoni, D.M., Perry, M.J.: Underwater gliders for ocean research. Marine Tech. Soc. J. 38(2), 73–84 (2004)CrossRef
  21.Smith, R.N., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. Int. J. Rob. Res. 29(12), 1475–1497 (2010)CrossRef
  22.Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNet CrossRef MATH
  23.Zaharie, D.: Influence of crossover on the behavior of differential evolution algorithms. Appl. Soft Comput. 9(3), 1126–1138 (2009)CrossRef
  24.Zamuda, A., Brest, J., Mezura-Montes, E.: Structured population size reduction differential evolution with multiple mutation strategies on CEC 2013 real parameter optimization. In: 2013 IEEE Conference on Evolutionary Computation, vol. 1, pp. 1925–1931, 20–23 June 2013
  25.Zamuda, A., Sosa, J.D.H.: Underwater glider path planning and population reduction in differential evolution. In: Fifteenth International Conference on Computer Aided Systems Theory, Museo Elder de la Ciencia y la Tecnologa, Las Palmas de Gran Canaria, Canary Islands, Spain, 8–13 February 2015, pp. 274–275 (2015)
  26.Zamuda, A., Sosa, J.D.H.: Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures. Appl. Soft Comput. 24, 95–108 (2014)CrossRef
  
• 作者单位：Aleš Zamuda (16)
  José Daniel Hernández-Sosa (17)
  
  16. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000, Maribor, Slovenia
  17. Institute of Intelligent Systems and Numerical Applications in Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain
  
• 丛书名：Computer Aided Systems Theory ᾿EUROCAST 2015
• ISBN：978-3-319-27340-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This paper presents an approach to underwater glider path planning (UGPP), where the population size reduction mechanism is introduced into the differential evolution (DE) meta-heuristic and two types of DE strategies (DE/best and DE/rand) are applied interchangeably. The newly proposed DE instance algorithms using population size reduction on the best and rand DE strategies are assessed and compared on 12 test scenarios using the proposed approach. A Bonferroni-Dunns statistical hypothesis testing is conducted to confirm out-performance of the favoured DE/best strategy over the DE/rand strategy for the 12 UGGP scenarios utilized. The analysis suggests that the approach can benefit from gradually reducing the population size and also tuning the DE parameters. Thereby, this contributes to extend the operational capabilities of the glider vehicle and to improve its value as a marine sensor, facilitating the implementation of flexible sampling schemes.