Solving multi-objective optimisation problems using the potential pareto regions evolutionary algorithm

Authors:
Nasreddine Hallam;Graham Kendall;Peter Blanchfield
Affiliations:
School of Computer Science and IT, The Univeristy of Nottingham;School of Computer Science and IT, The Univeristy of Nottingham;School of Computer Science and IT, The Univeristy of Nottingham
Venue:
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Year:
2006

Citing 2
Cited 0

Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a novel multi-objective evolutionary algorithm that we call Potential Pareto Regions Evolutionary Algorithm (PPREA). Unlike state-of-the-art algorithms, which use a fitness assignment method based on Pareto ranking, the approach adopted in this work is new. The fitness of an individual is equal to the least improvement needed by that individual in order to reach non-dominance status. This new algorithm is compared against the Nondominated Sorting Genetic Algorithm (NSGA-II) on a set of test suite problems derived from the works of researchers from MOEA community.