A dynamical system perspective on evolutionary heuristics applied to space trajectory optimization problems

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Abstract

In this paper we propose a generalized formulation of the evolutionary heuristic governing the movement of the individuals of Differential Evolution in the search space. The basic heuristic of Differential Evolution is casted in form of discrete dynamical system and extended to improve local convergence. It is demonstrated that under some assumptions on the local structure of the objective function, the proposed dynamical system, has fixed points towards which it converges asymptotically. This property is used to derive an algorithm that performs better than standard Differential Evolution on some space trajectory optimization problems. The novel algorithm is then extended with a guided restart procedure that further increases the performance reducing the probability of stagnation in deceptive local minima.
Original languageEnglish
Title of host publication2009 IEEE Congress on evolutionary computation
PublisherIEEE
Pages2340-2347
Number of pages7
Volume1-5
ISBN (Print)9781424429585
DOIs
Publication statusPublished - 18 May 2009
EventIEEE Congress on Evolutionary Computation - Trondheim, Norway
Duration: 18 May 200921 May 2009

Publication series

NameIEEE Congress on Evolutionary Computation
PublisherIEEE

Conference

ConferenceIEEE Congress on Evolutionary Computation
CountryNorway
CityTrondheim
Period18/05/0921/05/09

Keywords

  • global optimization
  • differential evolution
  • space trajectory optimization problems

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  • Cite this

    Vasile, M., Minisci, E., & Locatelli, M. (2009). A dynamical system perspective on evolutionary heuristics applied to space trajectory optimization problems. In 2009 IEEE Congress on evolutionary computation (Vol. 1-5, pp. 2340-2347). (IEEE Congress on Evolutionary Computation). IEEE. https://doi.org/10.1109/CEC.2009.4983232