Daniel Delahaye, Stephane Puechmorel ; series editor, Narendra Jussien
1 online resource (xv, 338 pages)
6.1.3. Dynamic assignment
Includes bibliographical references (page 299-326) and index
Title Page; Contents; Introduction; Part 1: Optimization and Artificial Evolution; Chapter 1: Optimization: State of the Art; 1.1. Methodological principles in optimization; 1.1.1. Introduction; 1.1.2. Modeling; 1.1.3. Complexity; 1.1.4. Computation time; 1.1.5. Conclusion; 1.2. Optimization algorithms; 1.2.1. Introduction; 1.2.2. Linear programming; 1.2.3. Nonlinear programming (NLP); 1.2.4. Local methods subject to constraints; 1.2.5. Deterministic global methods; 1.2.6. Stochastic global methods; 1.2.7. Genetic algorithms; 1.2.8. Conclusion; Chapter 2: Genetic Algorithms and Improvements
2.1. General points2.1.1. Introduction; 2.1.2. Principle of genetic algorithms; 2.1.3. Coding principles; 2.1.4. Random generation of the initial population; 2.1.5. Crossover operators; 2.1.6. Mutation operators; 2.1.7. Selection principles; 2.2. Classic improvements; 2.2.1. Scaling; 2.2.2. Sharing; 2.2.3. Crowding; 2.2.4. Memetic algorithms; 2.2.5. Multi-objective genetic algorithms; 2.3. Our contributions; 2.3.1. Adaptive clustered sharing; 2.3.2. Association of genetic algorithms with simulated annealing; 2.3.3. Parallel genetic algorithms; 2.4. Conclusion
5.5. 3D extension5.5.1. Introduction; 5.5.2. Mathematical modeling; 5.5.3. Application of artificial evolution to the problem; 5.5.4. Results; 5.5.5. Conclusion; 5.6. Accounting for the dynamic aspect; 5.6.1. Formalization of objectives and associated mathematical model; 5.6.2. Optimization using a genetic algorithm: continuous approach; 5.6.3. Optimization using a genetic algorithm: discreteapproach; Chapter 6: Contribution to Traffic Assignment; 6.1. Summary of traffic assignment methods based ontransportation network theory; 6.1.1. Transportation networks; 6.1.2. Static assignment
Chapter 3: A New Concept for Genetic Algorithms Based on Order Statistics3.1. Introduction; 3.2. Order statistics; 3.3. Estimating the probability that the global optimum belongs to a given domain; 3.4. Genetic algorithms and order statistics; 3.4.1. Introduction; 3.4.2. Coding; 3.4.3. Recombination operators; 3.4.4. Evaluation of fitness; 3.5. Application to test functions; 3.5.1. Results for the Griewank function; 3.5.2. Results for the Rosenbrook function; 3.5.3. Results for the Lennard-Jones function; 3.6. Conclusion; Part 2: Applications to Air Traffic Control
Chapter 4: Air Traffic ControlChapter 5: Contributions to Airspace Sectorization; 5.1. Introduction; 5.2. Modeling in 2D; 5.2.1. Model based on a transportation network; 5.2.2. Associated complexity; 5.3. Continuous modeling; 5.3.1. Principle; 5.3.2. Chromosome coding; 5.3.3. Initial population generation principle; 5.3.4. Crossover operator; 5.3.5. Mutation operator; 5.3.6. Calculation and normalization of the fitness function; 5.3.7. Results; 5.3.8. Conclusion; 5.4. Discrete modeling; 5.4.1. Principle; 5.4.2. Coding; 5.4.3. Recombination operators; 5.4.4. Results; 5.4.5. Conclusion
0
8
8
8
8
This book combines the research activities of the authors, both of whom are researchers at Ecole Nationale de l'Aviation Civile (French National School of Civil Aviation), and presents their findings from the last 15 years. Their work uses air transport as its focal point, within the realm of mathematical optimization, looking at real life problems and theoretical models in tandem, and the challenges that accompany studying both approaches.The authors' research is linked with the attempt to reduce air space congestion in Western Europe, USA and, increasingly, Asia