Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms

Wenjing Zhao
  • Function : Author
Jiangjun Tang
  • Function : Author
Sameer Alam
  • Function : Author
Axel Bender
  • Function : Author
Hussein A. Abbass
  • Function : Author

Abstract

Usually, Evolutionary Computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposed - Multiobjective Evolutionary Based Risk Assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented ("scenario Representation") and evolved ("scenario Generation") in order to evaluate algorithms ("scenario Evaluation"). The objective functions aim at maximising or minimising the success rate of an algorithm. In the "scenario Mining" step, MEBRA identifies the patterns common in problem instances when an algorithm performs best in order to understand when to use it, and in instances when it performs worst in order to understand when not to use it. So far, MEBRA has only been applied to a limited number of problems. Here we demonstrate its viability to efficiently detect hot spots in an algorithm's problem space. In particular, we apply the basic MEBRA rationale in the area of Air Traffic Management (ATM). We examine two widely used algorithms for Aircraft Landing Sequencing: First Come First Served (FCFS) and Constrained Position Shifting (CPS). Through the use of three different problem ("scenario") representations, we identify those patterns in ATM problems that signal instances when CPS performs better than FCFS, and those when it performs worse. We show that scenario representation affects the quality of MEBRA outputs. In particular, we find that the variable-length chromosome representation of aircraft scheduling sequence scenarios converges fast and finds all relevant risk patterns associated with the use of FCFS and CPS.
Fichier principal
Vignette du fichier
final_04.pdf (193.78 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01054494 , version 1 (07-08-2014)

Licence

Attribution

Identifiers

Cite

Wenjing Zhao, Jiangjun Tang, Sameer Alam, Axel Bender, Hussein A. Abbass. Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms. 7th IFIP TC 10 Working Conference on Distributed, Parallel and Biologically Inspired Systems (DIPES) / 3rd IFIP TC 10 International Conference on Biologically-Inspired Collaborative Computing (BICC) / Held as Part of World Computer Congress (WCC) , Sep 2010, Brisbane, Australia. pp.254-265, ⟨10.1007/978-3-642-15234-4_25⟩. ⟨hal-01054494⟩
112 View
234 Download

Altmetric

Share

Gmail Facebook X LinkedIn More