Testing parallelization paradigms for MOEAs

Authors:
Sadeesha Gamhewa;Philip F. Hingston
Affiliations:
Edith Cowan University, Perth, Australia;Edith Cowan University, Perth, Australia
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 4
Cited 0

A scalable multi-objective test problem toolkit

EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms

IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we report on our investigation of factors affecting the performance of various parallelization paradigms for multi-objective evolutionary algorithms. Different parallelization paradigms emphasize separate development of sub-populations versus communication and coordination between sub-populations to greater or lesser degrees. We hypothesized that the characteristics of a particular problem will favour some paradigms over others. We tested this hypothesis by creating variations on test problems with different characteristics, and testing the performance of different paradigms in a cluster environment.