Paper Info
Title
Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front
Abstract
In evolutionary multi-objective optimization (EMO), the convergence to the Pareto set of a multi-objective optimization problem (MOP) and the diversity of the final approximation of the Pareto front are two important issues. In the existing definitions and analyses of convergence in multi-objective evolutionary algorithms (MOEAs), convergence with probability is easily obtained because diversity is not considered. However, diversity cannot be guaranteed. By combining the convergence with diversity, this paper presents a new definition for the finite representation of a Pareto set, the B-Pareto set, and a convergence metric for MOEAs. Based on a new archive-updating strategy, the convergence of one such MOEA to the B-Pareto sets of MOPs is proved. Numerical results show that the obtained B-Pareto front is uniformly distributed along the Pareto front when, according to the new definition of convergence, the algorithm is convergent.
Year
DOI
Venue
2011
10.1016/j.ins.2011.04.004
Inf. Sci.
Keywords
Field
DocType
multi-objective optimization problem,b-pareto front,pareto front,evolutionary multi-objective optimization,b-pareto set,existing definition,multi-objective evolutionary algorithm,pareto set,new definition,new archive-updating strategy,evolutionary algorithm,convergence,multi objective optimization
Convergence (routing),Mathematical optimization,Evolutionary algorithm,Multi-objective optimization,Artificial intelligence,Distributed representation,Optimization problem,Mathematics,Pareto principle,Machine learning
Journal
Volume
Issue
ISSN
181
16
0020-0255
Citations 
PageRank 
References 
17
0.68
47
Authors
3
Name
Order
Citations
PageRank
Yu Chen151749.61
Xiufen Zou227225.44
Wei-Cheng Xie39412.05