Skip to main content
Springer Nature Link
Log in

Optimization of Aircraft Landing Route and Order Based on Novelty Search

  • Conference paper
  • First Online:
Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

  • 1125 Accesses

Abstract

This paper focuses on the Aircraft Landing Problem (ALP) and proposes the efficient aircraft landing route and order optimization method compared to the conventional method. As a difficulty in solving ALP, both landing route and order of all aircrafts should be optimized together, meaning that they cannot be optimized independently. To tackle this problem, our method employs novelty search to generate variety candidates of aircraft landing routes, which are indispensable to generate the feasible landing order of all aircraft. Through the experiment on a benchmark problem, it has revealed that the proposed method can reduce the occupancy time of aircrafts in an airport.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Icao, “Annual Report of the Council,” Int. Civ. Aviat. Organ., 2011.

    Google Scholar 

  2. J. a. Bennell, M. Mesgarpour, and C. N. Potts, “Airport runway scheduling,” Ann. Oper. Res., vol. 204, no. 1, pp. 249–270, 2013.

    Google Scholar 

  3. T. Tajima, K. Nakano, and M. Ichikawa, “A Real-Time Path Planning Using Genetic Algorithms.” The journal of the Japanese Society for Artificial Intelligence (Volume: 10, Issue 14) pp 94–104 1995.

    Google Scholar 

  4. X. B. Hu and W. H. Chen, “Genetic algorithm based on receding horizon control for arrival sequencing and scheduling,” Eng. Appl. Artif. Intell., vol. 18, no. 5, pp. 633–642, 2005.

    Google Scholar 

  5. K. Treleaven and Z. H. Mao, “Conflict resolution and traffic complexity of multiple intersecting flows of aircraft,” IEEE Trans. Intell. Transp. Syst., vol. 9, no. 4, pp. 633–643, 2008.

    Google Scholar 

  6. A. Murata, M. Nakata, H. Sato, T. Kovacs, and K. Takadama, “Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation,” Proc. 9th EAI Int. Conf. Bio-inspired Inf. Commun. Technol. (formerly BIONETICS), 2016.

    Google Scholar 

  7. J. Lehman, “Evolution Through the Search for Novelty,” p. 223, 2007.

    Google Scholar 

  8. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.

    Google Scholar 

  9. E. Naredo and L. Trujillo, “Searching for Novel Clustering Programs,” Proc. 15th Annu. Conf. Genet. Evol. Comput. - GECCO 2013, pp. 1093–1100, 2013.

    Google Scholar 

  10. J. B. Mouret, “Novelty-Based Multiobjectivization,” Stud. Comput. Intell., vol. 341, pp. 139–154, 2011.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    Akinori Murata, Hiroyuki Sato & Keiki Takadama

Authors
  1. Akinori Murata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroyuki Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keiki Takadama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akinori Murata .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    George Leu

  2. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Hemant Kumar Singh

  3. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Saber Elsayed

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Murata, A., Sato, H., Takadama, K. (2017). Optimization of Aircraft Landing Route and Order Based on Novelty Search. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49049-6_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics