An ant system algorithm for automated trajectory planning

M. Ceriotti; M. Vasile

An ant system algorithm for automated trajectory planning

M. Ceriotti, M. Vasile

Mechanical And Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

Abstract

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.

Original 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

Keywords

multi gravity assist trajectories
ant system algorithm
automated trajectory planning

Access to Document

http://www.wcci2010.org/

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title = "An ant system algorithm for automated trajectory planning",

abstract = "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.",

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AU - Vasile, M.

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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

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T3 - IEEE Congress on Evolutionary Computation

BT - 2010 Congress on evolutionary computation (CEC)

PB - IEEE

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T2 - World Congress on Computational Intelligence, WCCI 2010

Y2 - 18 July 2010 through 23 July 2010

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