articleEvolutionary ComputationSep 1, 2002Closed access

Combining Convergence and Diversity in Evolutionary Multiobjective Optimization

MLMarco LaumannsLTLothar ThieleKDKalyanmoy DebEZEckart Zitzler

École Polytechnique Fédérale de Lausanne · Indian Institute of Technology Kanpur

PubMed
Indexed incrossrefpubmed

Abstract

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of epsilon-dominance, new archiving strategies…

Citation impact

1,479
total citations
FWCI
27.65
Percentile
100%
References
35
Citations per year

Authors

4

Topics & keywords

Topics
Keywords
  • Evolutionary algorithm
  • Mathematical optimization
  • Convergence (economics)
  • Pareto principle
  • Multi-objective optimization
  • Computer science
  • Pareto optimal
  • Optimization problem
No related works found for this paper.

Funding