Title

Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms

Document Type

Conference paper

Source Publication

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

Publication Date

12-8-2003

Volume

4

First Page

2498

Last Page

2505

Publisher

IEEE Computer Society

Abstract

The issue of obtaining a well-converged and well-distributed set of Pareto optimal solutions efficiently and automatically is crucial in multiobjective evolutionary algorithms (MOEAs). Many studies have proposed different evolutionary algorithms that can progress towards Pareto optimal sets with a wide-spread distribution of solutions. However, most mathematically convergent MOEAs desire certain prior knowledge about the objective space in order to efficiently maintain widespread solutions. We propose, based on our novel E-dominance concept, an adaptive rectangle archiving (ARA) strategy that overcomes this important problem. The MOEA with this archiving technique provably converges to well-distributed Pareto optimal solutions without prior knowledge. ARA complements the existing archiving techniques, and is useful to both researchers and practitioners.

DOI

10.1109/CEC.2003.1299402

Publisher Statement

Copyright © 2003 IEEE. Access to external full text or publisher's version may require subscription.

Additional Information

Paper presented at the Congress on Evolutionary Computation (CEC), Dec 08-12, 2003, Canberra, Australia.

Full-text Version

Publisher’s Version

Language

English

Share

COinS