Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms

Huidong JIN, Man Leung WONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

15 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
PublisherIEEE Computer Society
Pages2498-2505
Number of pages8
Volume4
DOIs
Publication statusPublished - 8 Dec 2003

Fingerprint

Evolutionary algorithms

Bibliographical note

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

Cite this

JIN, H., & WONG, M. L. (2003). Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms. In 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings (Vol. 4, pp. 2498-2505). IEEE Computer Society. https://doi.org/10.1109/CEC.2003.1299402
JIN, Huidong ; WONG, Man Leung. / Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms. 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 4 IEEE Computer Society, 2003. pp. 2498-2505
@inproceedings{4f7a83b815444dfa94fa53f303b92a1c,
title = "Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms",
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.",
author = "Huidong JIN and WONG, {Man Leung}",
note = "Paper presented at the Congress on Evolutionary Computation (CEC), Dec 08-12, 2003, Canberra, Australia.",
year = "2003",
month = "12",
day = "8",
doi = "10.1109/CEC.2003.1299402",
language = "English",
volume = "4",
pages = "2498--2505",
booktitle = "2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings",
publisher = "IEEE Computer Society",
address = "United States",

}

JIN, H & WONG, ML 2003, Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms. in 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. vol. 4, IEEE Computer Society, pp. 2498-2505. https://doi.org/10.1109/CEC.2003.1299402

Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms. / JIN, Huidong; WONG, Man Leung.

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 4 IEEE Computer Society, 2003. p. 2498-2505.

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

TY - GEN

T1 - Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms

AU - JIN, Huidong

AU - WONG, Man Leung

N1 - Paper presented at the Congress on Evolutionary Computation (CEC), Dec 08-12, 2003, Canberra, Australia.

PY - 2003/12/8

Y1 - 2003/12/8

N2 - 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.

AB - 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.

UR - http://commons.ln.edu.hk/sw_master/6834

U2 - 10.1109/CEC.2003.1299402

DO - 10.1109/CEC.2003.1299402

M3 - Conference paper (refereed)

VL - 4

SP - 2498

EP - 2505

BT - 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

PB - IEEE Computer Society

ER -

JIN H, WONG ML. Adaptive diversity maintenance and convergence guarantee in multiobjective evolutionary algorithms. In 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. Vol. 4. IEEE Computer Society. 2003. p. 2498-2505 https://doi.org/10.1109/CEC.2003.1299402