Name
Title | ||
|---|---|---|
Dynamic Distributed Genetic Algorithm Using Hierarchical Clustering for Flight Trajectory Optimization of Winged Rocket |
Abstract | ||
|---|---|---|
The development of an efficient and flexible guidance system is one of the most important aspects of studies on reusable space transportation systems such as winged rockets. We therefore propose a flight path generation method that uses a dynamic distributed genetic algorithm. This method dynamically divides and merges the individuals of some groups and thus maintains diversity in its optimization solutions. Although most conventional studies on distributed genetic algorithms had almost the same objective, the number of groups into which the individuals were divided was fixed. This constraint deteriorated the growth of the solution diversity because the number of groups is closely related to the classification of the individuals. Our proposed dynamic distributed genetic algorithm, which used hierarchical clustering, was verified by computer simulation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICMLA.2013.60 | ICMLA |
Keywords | Field | DocType |
hierarchical clustering,genetic algorithms,trajectory optimization | Data mining,Trajectory optimization,Computer science,Pattern clustering,Artificial intelligence,Guidance system,Genetic algorithm,Hierarchical clustering,Simulation,Meta-optimization,Aerospace simulation,Rocket,Machine learning | Conference |
Volume | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miyamoto, S. | 1 | 0 | 0.68 |
| Takaaki Matsumoto | 2 | 12 | 2.37 |
| koichi yonemoto | 3 | 1 | 2.09 |