Recent Developments in Metaheuristics

Question

1. What are the contributions in "Hybrid metaheuristic for air traffic management with uncertainty" ?

2. What have the authors stated for future works in "Hybrid metaheuristic for air traffic management with uncertainty" ?

3. What are the benefits of the new ATM systems?

4. What is the objective function to be minimized in a simulated annealing algorithm?

Accepted Answer

To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations ( TBO ), that will require aircraft to follow an assigned 4D trajectory ( time-constrained trajectory ) with high precision.. The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories.

Accepted Answer

The authors introduced an efficient methodology to address the strategic trajectory planning problem in the framework of future trajectory-based ATM operations assuming time uncertainty on the position of the aircraft along its 4D trajectory.. Further research should concentrate on reducing the extra route length, and the number of flight level shifts, and of departure-time shifts, instead of being content with ( possibly costly ) interactionfree solutions.. To reduce the number of sampling points needed while minimizing further the computation time, this interaction-detection method interpolates the aircraft position between two suspected sampling points instead of refining the sampling-time step.. The simulated annealing part ensures diversity of the candidate solutions considered, while the local-search module intensifies the search in promising regions of the feasible domain in order to accelerate convergence.

Accepted Answer

With the soon-coming technologies that will enable more powerful communication systems, more precise surveillance systems, and more reliance automated support tools, these new ATM systems will improve safety, reduce delay and aviation pollution emissions, while maximizing the use of airspace capacity.

Accepted Answer

In the simulated annealing optimization algorithm, the objective function to be minimized is analogical to the energy of the physical problem, while the decision variables of the problem correspond to the coordinates of the material’s particles.

Accepted Answer

Conflict detection methods can be roughly classified into three categories [16]: nominal, worst-case, and probabilistic conflict detections, according to the assumptions made on the predicted aircraft trajectory.

Accepted Answer

Congestion in the airspace sectors is minimized by allocating to each flight optimal departure times and alternative routes (based on route-beacons navigation) using genetic algorithms (GA).

Accepted Answer

In [4], a ground holding is assigned to each aircraft and an optimal flight level is subsequently allocated to each flow of aircraft using constraint programming.

Accepted Answer

For instance, the best solution obtained from one metaheuristic algorithm can be used as an initial solution for another metaheuristic algorithm.

Accepted Answer

the probability of carrying out both SA and the local search (successively), PSL, is simply:PSL(T ) = 1− (PSA(T ) + PLoc(T )) (10)A key factor in tuning this hybrid algorithm is to reach a good trade-off between exploration (diversification) and exploitation (intensification) of the solution space, i.e. a compromise between fine convergence towards local minima, and the computation time invested in exploring the whole search space in order not to miss a global optimum.

Accepted Answer

Another challenging research track is to take into account equity between airlines in the trajectory optimization process when deciding which trajectory is to be modified in order to avoid a conflict.

Accepted Answer

The optimal arcs and nodes (in a 3D-mesh network), speeds, and departure/arrival times for each flight are obtained by an exact deterministic method.

Accepted Answer

the total interaction between trajectories, Φtot(u), for a whole N -aircraft traffic situation is simply defined as:Φtot(u) = N∑ i=1 Φi(u) = N∑ i=1 Ki∑ k=1 Φi,k(u). (7)One wishes to determine values for the optimization variables δi, li, and wi for each flight i = 1, 2, . . . , N so as to minimize the total interaction, Φtot(u), between the N given trajectories.

Accepted Answer

To evaluate the objective function, Φtot, at a candidate solution, u, one needs to compute the interaction at each possible pair of sampled trajectory points involved in N aircraft trajectories.

Accepted Answer

Their optimal ground-holding times and alternative routes are obtained by solving a 0-1 integer model taking into account airspace sector capacity.

Recent Developments in Metaheuristics

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Most frequently asked questions

1. What are the contributions in "Hybrid metaheuristic for air traffic management with uncertainty" ?

2. What have the authors stated for future works in "Hybrid metaheuristic for air traffic management with uncertainty" ?

3. What are the benefits of the new ATM systems?

4. What is the objective function to be minimized in a simulated annealing algorithm?

5. What are the three categories of conflict detection methods?

6. How are the authors able to minimize congestion in the airspace sectors?

7. What is the way to allocate a flight level to each flow of aircraft?

8. What is the way to use the solution obtained from one metaheuristic algorithm?

9. What is the probability of carrying out both SA and local search?

10. What is the challenging research track?

11. How are the optimal arcs and nodes obtained?

12. What is the simplest way to minimize the interaction between trajectories?

13. How do the authors compute the interaction at each possible pair of trajectory points?

14. How are the optimal ground-holding times and alternative routes obtained?

Figures

Citations

Recent Advances in Selection Hyper-heuristics

A comprehensive survey on recent metaheuristics for feature selection

A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems

Multi-objective temporal bin packing problem: An application in cloud computing

Multivector particle swarm optimization algorithm

References

Metaheuristics: From Design to Implementation

A review of conflict detection and resolution modeling methods

The Air Traffic Flow Management Problem with Enroute Capacities

A probabilistic approach to aircraft conflict detection

The Flow Management Problem in Air Traffic Control

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