Jean-Marc Alliot

Automatic aircraft conflict resolution using genetic algorithms

In this paper, we show how genetic algorithms can be used to solve en-route aircraft conflict automatically to increase Air Traffic Control capacity in high density areas. The ATC _ background and the model are presented. The complexity of the problem is then discussed. The author then justifies the choice of GAs. After a brief description of genetic algorithms, the author describes the improvements that were used for solving the conflict resolution problem. Several numerical applications are then given justifying the choices that were made and illustrating the interest of the research. Next steps of this work are discussed in the conclusion.

Airspace sectoring by evolutionary computation

This paper addresses the classical graph partitioning problem applied to the air network. We consider an air transportation network with aircraft inducing a control workload. This network has to be partitioned into K balanced sectors for which the cutting flow is minimized.

Neural Nets Trained by Genetic Algorithms for Collision Avoidance

As air traffic keeps increasing, many research programs focus on collision avoidance techniques. For short or medium term avoidance, new headings have to be computed almost on the spot, and feed forward neural nets are susceptible to find solutions in a much shorter amount of time than classical avoidance algorithms (A*, stochastic optimization, etc.) In this article, we show that a neural network can be built with unsupervised learning to compute nearly optimal trajectories to solve two aircraft conflicts with the highest reliability, while computing headings in a few milliseconds.

Turbo codes optimization using genetic algorithms

Turbo codes have been an important revolution in the digital communications world. Since their discovery, the coding community has been trying to understand, explain and improve turbo codes. The floor phenomenon is the parallel concatenated convolutional turbo codes main problem. In this paper, genetic algorithms are used to lower the free distance of such a code. Results in terms of bit error rate are compared to the other main methods.

Optimization of air traffic control sector configurations using tree search methods and genetic algorithms

The paper describes two classical tree search algorithms and a genetic algorithm for the Air Traffic Flow Management process that take as input the traffic flows and build optimal sector configurations considering the airspace capacity constraints and also the maximum number of control positions that can be manned at each time of the day. This optimization is made in a realistic context, using the airspace description data, the traffic data, the number of available control positions, and the sector capacities of the French ATC centers.

CATS: A Complete Air Traffic Simulator

The main ideas behind the design of CATS were to have a general purpose simulator able to be a stable basis for the development of other modules, representing different levels of Air Traffic Control and Air Traffic Management. The basic framework and hypothesis of the CATS simulator are introduced. Two approaches of automatic conflict resolution are detailed as examples. The first one deals with a centralized control system for high density areas and medium term (10 to 15 minutes ahead) control. The second one deals with an on board reactive short term (3 to 10 minutes ahead) automatic conflict solver. As both solvers use a conflict detection that is robust to uncertainties, a convex modeling of uncertainties is first introduced.

OPTIMAL RESOLUTION OF EN ROUTE CONFLICTS.

In this article, we present mathematical modeling for en route conflict resolution, discuss the mathematical complexity of this problem, and show that classical mathematical optimization techniques are not suited to solve it. Instead, a stochastic optimization algorithm based on genetic techniques is presented. This algorithm can find many different nearly optimal solutions in real time, even in very complex situations involving many aircraft. Different examples of resolution with highly loaded traffic are presented and the results of the solver on an air traffic controller simulation prototype with real flight plans are evaluated. Limitations of the system and possible improvements are discussed.

Finding and proving the optimum: cooperative stochastic and deterministic search

In this article, we introduce a global cooperative approach between an Interval Branch and Bound Algorithm and an Evolutionary Algorithm, that takes advantage of both methods to optimize a function for which an inclusion function can be expressed. The Branch and Bound algorithm deletes whole blocks of the search space whereas the Evolutionary Algorithm looks for the optimum in the remaining space and sends to the IBBA the best evaluation found in order to improve its Bound. The two algorithms run independently and update common information through shared memory. The cooperative algorithm prevents premature and local convergence of the evolutionary algorithm, while speeding up the convergence of the branch and bound algorithm. Moreover, the result found is the proved global optimum. In part 1, a short background is introduced. Part 2.1 describes the basic Interval Branch and Bound Algorithm and part 2.2 the Evolutionary Algorithm. Part 3 introduces the cooperative algorithm and part 4 gives the results of the algorithms on benchmark functions. The last part concludes and gives suggestions of avenues of further research.

Preventing Premature Convergence and Proving the Optimality in Evolutionary Algorithms

Evolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their exponential complexity and their inability to quickly compute a good approximation of the global minimum. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a branch and bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and is highly competitive with both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality.

Arithmetic simulation and performance metrics [ATC]

Performance metrics in the air traffic management (ATM) community are becoming a strategic issue, and they are getting more and more attention. However, defining such metrics is a difficult problem. In this paper, we show how arithmetic simulations can be used to give performance information. We also point out that many different metrics can be defined, each of them giving different results regarding efficiency. We conclude that an extreme caution must be applied when interpreting results.