UEGO

UEGO, an abstract clustering technique for multimodal global optimization In this paper, UEGO, a new general technique for accelerating and/or parallelizing existing search methods is suggested. The skeleton of the algorithm is a parallel hill climber. The separate hill climbers work in restricted search regions (or clusters) of the search space. The volume of the clusters decreases as the search proceeds which results in a cooling effect similar to simulated annealing. Besides this, UEGO can be effectively parallelized; the communication between the clusters is minimal. The purpose of this communication is to ensure that one hill is explored only by one hill climber. UEGO makes periodic attempts to find new hills to climb. Empirical results are also presented which include an analysis of the effects of the user-given parameters and a comparison with a hill climber and a GA.


References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. García-Martínez, J.M.; Garzón, E.M.; Cecilia, J.M.; Pérez-Sánchez, H.; Ortigosa, P.M.: An efficient approach for solving the HP protein folding problem based on UEGO (2015)
  2. Redondo, J.L.; Arrondo, A.G.; Fernández, J.; García, I.; Ortigosa, P.M.: A two-level evolutionary algorithm for solving the facility location and design $(1|1)$-centroid problem on the plane with variable demand (2013)
  3. Veloso de Melo, Vinícius; Botazzo Delbem, Alexandre Cláudio: Investigating smart sampling as a population initialization method for differential evolution in continuous problems (2012)
  4. Li, Minqiang; Lin, Dan; Kou, Jisong: An investigation on niching multiple species based on population replacement strategies for multimodal functions optimization (2010)
  5. Redondo, J.L.; Fernández, J.; García, I.; Ortigosa, P.M.: Heuristics for the facility location and design $(1|1)$-centroid problem on the plane (2010)
  6. Redondo, J.L.; Fernández, J.; García, I.; Ortigosa, P.M.: A robust and efficient algorithm for planar competitive location problems (2009)
  7. Redondo, J.L.; Fernández, J.; García, I.; Ortigosa, P.M.: A robust and efficient algorithm for planar competitive location problems (2009)
  8. Li, Minqiang; Kou, Jisong: Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization (2008)
  9. Redondo, Juana L.; Fernández, José; García, Inmaculada; Ortigosa, Pilar M.: Parallel algorithms for continuous competitive location problems (2008)
  10. Ortigosa, P.M.; Redondo, J.L.; García, I.; Fernández, J.J.: A population global optimization algorithm to solve the image alignment problem in electron crystallography (2007)
  11. Oliveira, Alexandre C.M.; Lorena, Luiz A.N.: Detecting promising areas by evolutionary clustering search (2004)
  12. Benvenuti, L.; Di Benedetto, M.D.; Di Gennaro, S.; Sangiovanni-Vincentelli, A.: Individual cylinder characteristic estimation for a spark injection engine (2003)
  13. González-Linares, J.M.; Guil, N.; Zapata, E.L.; Ortigosa, P.M.; García, I.: Parallelization of an algorithm for the automatic detection of deformable objects (2001)
  14. Jelasity, Márk; Martínez Ortigosa, Pilar; García, Inmaculada: UEGO, an abstract clustering technique for multimodal global optimization (2001)
  15. Ortigosa, Pilar M.; García, I.; Jelasity, Márk: Reliability and performance of UEGO, a clustering-based global optimizer (2001)
  16. Ortigosa, P.M.; García, I.; Jelásity, M.: Two approaches for parallelizing the UEGO algorithm (2001)