Abstract
| Language | English |
|---|---|
| Title of host publication | 2010 Congress on evolutionary computation (CEC) |
| Place of Publication | New York |
| Publisher | IEEE |
| ISBN (Print) | 9781424481262 |
| Publication status | Published - 18 Jul 2010 |
| Event | World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010 |
Publication series
| Name | IEEE Congress on Evolutionary Computation |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 1556-603X |
Conference
| Conference | World Congress on Computational Intelligence, WCCI 2010 |
|---|---|
| City | Barcelona, Spain |
| Period | 18/07/10 → 23/07/10 |
Fingerprint
Keywords
- multi gravity assist trajectories
- ant system algorithm
- automated trajectory planning
Cite this
}
An ant system algorithm for automated trajectory planning. / Ceriotti, M.; Vasile, M.
2010 Congress on evolutionary computation (CEC). New York : IEEE, 2010. (IEEE Congress on Evolutionary Computation).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - An ant system algorithm for automated trajectory planning
AU - Ceriotti, M.
AU - Vasile, M.
PY - 2010/7/18
Y1 - 2010/7/18
N2 - The paper presents an Ant System based algorithm to optimally plan multi-gravity assist trajectories. The algorithm is designed to solve planning problems in which there is a strong dependency of one decision one all the previously made decisions. In the case of multi-gravity assist trajectories planning, the number of possible paths grows exponentially with the number of planetary encounters. The proposed algorithm avoids scanning all the possible paths and provides good results at a low computational cost. The algorithm builds the solutionincrementally, according to Ant System paradigms. Unlikestandard ACO, at every planetary encounter, each ant makes a decision based on the information stored in a tabu and feasible list. The approach demonstrated to be competitive, on a number of instances of a real trajectory design problem, against known GA and PSO algorithms.
AB - The paper presents an Ant System based algorithm to optimally plan multi-gravity assist trajectories. The algorithm is designed to solve planning problems in which there is a strong dependency of one decision one all the previously made decisions. In the case of multi-gravity assist trajectories planning, the number of possible paths grows exponentially with the number of planetary encounters. The proposed algorithm avoids scanning all the possible paths and provides good results at a low computational cost. The algorithm builds the solutionincrementally, according to Ant System paradigms. Unlikestandard ACO, at every planetary encounter, each ant makes a decision based on the information stored in a tabu and feasible list. The approach demonstrated to be competitive, on a number of instances of a real trajectory design problem, against known GA and PSO algorithms.
KW - multi gravity assist trajectories
KW - ant system algorithm
KW - automated trajectory planning
UR - http://www.wcci2010.org/
M3 - Conference contribution book
SN - 9781424481262
T3 - IEEE Congress on Evolutionary Computation
BT - 2010 Congress on evolutionary computation (CEC)
PB - IEEE
CY - New York
ER -