Abstract:
Airspace safety is fundamental to any air traffic management system (ATM). On tactical level it involves safe separation between two aircraft (conflict detection) and on strategic level it involves managing risk of collision in an airspace. However, the non-linear, stochastic and time-dependent inter-dependency among components in the ATM system make classical mathematical techniques obsolete to effectively model and investigate them. In this presentation, I will demonstrate how nature-inspired techniques such as evolutionary computation and genetic algorithms are an effective approach to address such complex problems of ATM to which traditional methodologies are ineffective or infeasible. I will present two case studies from my research, conflict detection algorithms evaluation and airspace collision risk hot-spots identification, to demonstrate how nature inspired computation can provide interesting insights into a complex system.

Bio:
Sameer is a senior lecturer in aviation at the University of New South Wales, Australian Defense Force Academy campus in Canberra. He holds Masters and PhD degree in computer science and artificial intelligence respectively. Currently, he is a visiting scientist at Ecole Nationale de l'Aviation Civil (ENAC) in Toulouse, France. He is also the Chief Investigator for the ICAO Middle-East collision risk modelling project and served as CI/Co-CI for several research projects for Air Services Australia and Eurocontrol. His interest include  fishing, complex network modelling, flying, evolutionary computation, hiking, genetic algorithm, swarm intelligence in the same order.

 

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