Pareto meta-heuristics for generating safe flight trajectories under weather hazards

  • Authors:

  • Sameer Alam;Lam T. Bui;Hussein A. Abbass;Michael Barlow

  • Affiliations:

  • 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, Australia;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, Australia;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, Australia;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, Australia

  • Venue:
  • SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
  • Year:
  • 2006

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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.