Late Parallelization and Feedback Approaches for Distributed Computation of Evolutionary Multiobjective Optimization Algorithms

ALTINÖZ Ö. T., Deb K.

2nd International Conference on Soft Computing and Machine Intelligence (ISCMI), Hong Kong, PEOPLES R CHINA, 23 - 24 Kasım 2015, ss.40-44 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/iscmi.2015.34
  • Basıldığı Şehir: Hong Kong
  • Basıldığı Ülke: PEOPLES R CHINA
  • Sayfa Sayıları: ss.40-44
  • Anahtar Kelimeler: reference point-based NSGA-II, parallelization, distributing computing
  • Ankara Üniversitesi Adresli: Evet

Özet

Distributing of the multiobjective optimization algorithm into various devices in a parallel fashion is a method for speeding up the computation time of the multiobjective evolutionary algorithms (MOEAs). When the processors are increased in number, the gain from parallelization decreases. Therefore, the aim of the parallelization method is not only to decrease the overall algorithm execution time, but also to obtain a higher gain from the use of parallel processors. Therefore, in this study two new parallelization approaches are proposed and discussed, which are named as late parallelization (no-migration approach) and feedback approaches. The performances of these approaches are evaluated on convex and concave multi-objective test problems.