Skip to Main content Skip to Navigation
Journal articles

Parallel predator-prey interaction for evolutionary multi-objective optimization

Grimme Christian 1 Joachim Lepping 2, * Papaspyrou Alexander 1 
* Corresponding author
2 MOAIS - PrograMming and scheduling design fOr Applications in Interactive Simulation
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Over the last decade, the predator-prey model (PPM) has emerged as an alternative algorithmic approach to multi-objective evolutionary optimization, featuring a very simple abstraction from natural species interplay and extensive parallelization potential. While substantial research has been done on the former, we for the first time review the PPM in the light of parallelization: We analyze the architecture and classify its components with respect to a recent taxonomy for parallel multi-objective evolutionary algorithms. Further, we theoretically examine benefits of simultaneous predator collaboration on a spatial population structure and give insights into solution emergence. On the prey level, we integrate a gradient-based local search mechanism to exploit problem independent parallelization and hybridize the model in order to achieve faster convergence and solution stability. This way, we achieve a good approximation and unfold further parallelization potential for the model.
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download
Contributor : Joachim Lepping Connect in order to contact the contributor
Submitted on : Wednesday, November 28, 2012 - 1:08:31 PM
Last modification on : Wednesday, July 6, 2022 - 4:11:57 AM
Long-term archiving on: : Saturday, December 17, 2016 - 4:10:21 PM


Files produced by the author(s)



Grimme Christian, Joachim Lepping, Papaspyrou Alexander. Parallel predator-prey interaction for evolutionary multi-objective optimization. Natural Computing, Springer Verlag, 2012, 11 (3), pp.519-533. ⟨10.1007/s11047-011-9266-9⟩. ⟨hal-00758211⟩



Record views


Files downloads