Hybrid behavioural-based multi-objective space trajectory optimization

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design.
LanguageEnglish
Title of host publicationMulti-Objective Memetic Algorithms
Pages231-253
Volume171
DOIs
Publication statusPublished - 2009

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Berlin Heidelberg
Volume171
ISSN (Print)1960-949X

Fingerprint

Multiobjective optimization
Trajectories
Decomposition

Keywords

  • global optimization
  • genetic algorithm
  • space trajectory design
  • multi-objective optimization

Cite this

Vasile, M. (2009). Hybrid behavioural-based multi-objective space trajectory optimization. In Multi-Objective Memetic Algorithms (Vol. 171, pp. 231-253). (Studies in Computational Intelligence; Vol. 171). https://doi.org/10.1007/978-3-540-88051-6
Vasile, Massimiliano. / Hybrid behavioural-based multi-objective space trajectory optimization. Multi-Objective Memetic Algorithms. Vol. 171 2009. pp. 231-253 (Studies in Computational Intelligence).
@inbook{19d9940a8470404aba70d4e445cda52c,
title = "Hybrid behavioural-based multi-objective space trajectory optimization",
abstract = "In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design.",
keywords = "global optimization, genetic algorithm, space trajectory design, multi-objective optimization",
author = "Massimiliano Vasile",
year = "2009",
doi = "10.1007/978-3-540-88051-6",
language = "English",
isbn = "978-3-540-88050-9",
volume = "171",
series = "Studies in Computational Intelligence",
publisher = "Springer Berlin Heidelberg",
pages = "231--253",
booktitle = "Multi-Objective Memetic Algorithms",

}

Vasile, M 2009, Hybrid behavioural-based multi-objective space trajectory optimization. in Multi-Objective Memetic Algorithms. vol. 171, Studies in Computational Intelligence, vol. 171, pp. 231-253. https://doi.org/10.1007/978-3-540-88051-6

Hybrid behavioural-based multi-objective space trajectory optimization. / Vasile, Massimiliano.

Multi-Objective Memetic Algorithms. Vol. 171 2009. p. 231-253 (Studies in Computational Intelligence; Vol. 171).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

TY - CHAP

T1 - Hybrid behavioural-based multi-objective space trajectory optimization

AU - Vasile, Massimiliano

PY - 2009

Y1 - 2009

N2 - In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design.

AB - In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design.

KW - global optimization

KW - genetic algorithm

KW - space trajectory design

KW - multi-objective optimization

UR - http://link.springer.com/content/pdf/10.1007%2F978-3-540-88051-6_11.pdf

U2 - 10.1007/978-3-540-88051-6

DO - 10.1007/978-3-540-88051-6

M3 - Chapter (peer-reviewed)

SN - 978-3-540-88050-9

VL - 171

T3 - Studies in Computational Intelligence

SP - 231

EP - 253

BT - Multi-Objective Memetic Algorithms

ER -

Vasile M. Hybrid behavioural-based multi-objective space trajectory optimization. In Multi-Objective Memetic Algorithms. Vol. 171. 2009. p. 231-253. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-540-88051-6