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Pareto Meta-heuristics for Generating Safe Flight Trajectories Under Weather Hazards

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

This paper compares ant colony optimization (ACO) and evolutionary multi-objective optimization (EMO) for the weather avoidance in a free flight environment. The problem involves a number of potentially conflicting objectives such as minimizing deviations, weather avoidance, minimizing distance traveled and hard constraints like aircraft performance. Therefore, we modeled the problem as a multi-objective problem with the aim of finding a set of non dominated solutions. This approach is expected to provide pilots the additional degree of freedom necessary for self optimized route planning in Free Flight. Experiments were conducted on a high fidelity air traffic simulator and results indicate that the ACO approach is better suited for this problem, due to its ability to generate solutions in early iterations as well as building better quality non dominated solutions over time.

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References

  1. NTSB (Aviation accidents statistical report: -2000) Washington D.C(2001) (1991)

    Google Scholar 

  2. RTCA: Report of the RTCA board of director’s select committee on Free Flight. Technical report, RTCA Inc., Washington, DC (1995)

    Google Scholar 

  3. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley, Chichester (2001)

    MATH  Google Scholar 

  4. Zitzler, E., Thiele, L., Deb, K.: Comparision of multiobjective evolutionary algorithms: Emprical results. Evolutionary Computation 8, 173–195 (2000)

    Article  Google Scholar 

  5. Dorigo, M., Stutlez, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  6. Lopez-Ibanez, M., Paquete, L., Stutzle, T.: On the Design of ACO for the Biobjective Quadratic Assignment Problem. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 214–225. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Alam, S., Abbass, H., Barlow, M., Lindsay, P.: Mapping lessons from ants to free flight: An ant-based weather avoidance algorithm in free flight airspace. In: Bender, A. (ed.) Proc. of of SPIE (Complex Systems), Brisbane, Qld, vol. 6039 (2005)

    Google Scholar 

  8. Alam, S., Abbass, H., Barlow, M.: Multi-objective ant colony optimization for weather avoidance in a free flight environment. Technical Report TR-ALAR-200603004, School of ITEE, UNSW@ADFA, Canberra, Australia (2006)

    Google Scholar 

  9. Peter, F.L.: Aviation Weather. Jepesson Sanderson, Inc. (1995)

    Google Scholar 

  10. Krozel, J.: Free Flight Research Issues and Literature Search. Technical Report NAS2-98005, NASA Ames Research Center, Moffett Field, CA 94035 (2000)

    Google Scholar 

  11. Krozel, J., Penny, S., Prete, J., Mitchell, J.: Comparison of algorithms for synthesizing weather avoidance routes in transition airspace. In: Proc. of the AIAA Guidance, Navigation, and Control Conference, 2004, Providence, RI (2004)

    Google Scholar 

  12. Mata, C., Mitchell, J.: A new algorithm for computing shortest paths in weighted planar subdivisions. In: Proc. of the 13th Annual ACM Symposium on Computational Geometry, Nice, France, pp. 264–273. ACM, New York (1997)

    Google Scholar 

  13. Bokadia, S., Valasek, J.: Severe weather avoidance using informed heuristic search. In: AIAA Guidance, Navigation, and Control Conference and Exhibit. Number AIAA-2001-4232, Montreal, Canada (2001)

    Google Scholar 

  14. Sugihara, K., Smith, J.: A genetic algorithm for 3-d path planning of a mobile robot. Technical Report 96-09-01, Software Engineering Research Laboratory, Department of Information and Computer Sciences, Univ. of Hawaii, Manoa (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Defence and Security Applications Research Centre, and The ARC Center for Complex Systems, University of New South Wales at The Australian Defence Force Academy, Canberra, ACT, 2600, Australia

    Sameer Alam, Lam T. Bui, Hussein A. Abbass & Michael Barlow

Authors
  1. Sameer Alam
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  2. Lam T. Bui
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  3. Hussein A. Abbass
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  4. Michael Barlow
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Alam, S., Bui, L.T., Abbass, H.A., Barlow, M. (2006). Pareto Meta-heuristics for Generating Safe Flight Trajectories Under Weather Hazards. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_104

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  • DOI: https://doi.org/10.1007/11903697_104

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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