A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm

Kata PRADITWONG; Xin YAO

doi:10.1007/978-3-540-74377-4_11

A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm

Kata PRADITWONG, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

8 Citations (Scopus)

Abstract

Many Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study [1] showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the Two-Archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the Two-Archive algorithm outperforms existing MOEAs on problems with a large number of objectives. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Computational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers
Editors	Yuping WANG, Yiu-ming CHEUNG, Hailin LIU
Publisher	Springer Berlin Heidelberg
Pages	95-104
Number of pages	10
ISBN (Electronic)	9783540743774
ISBN (Print)	9783540743767
DOIs	https://doi.org/10.1007/978-3-540-74377-4_11
Publication status	Published - 2007
Externally published	Yes
Event	2006 International Conference on Computational and Information Science, CIS 2006 - Guangzhou, China Duration: 3 Nov 2006 → 6 Nov 2006

Conference

Conference	2006 International Conference on Computational and Information Science, CIS 2006
Country/Territory	China
City	Guangzhou
Period	3/11/06 → 6/11/06

Keywords

Pareto Front
Multiobjective Optimization
Total Size
Strength Pareto Evolutionary Algorithm
Removal Strategy

Access to Document

10.1007/978-3-540-74377-4_11

Cite this

PRADITWONG, K., & YAO, X. (2007). A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm. In Y. WANG, Y. CHEUNG, & H. LIU (Eds.), Computational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (pp. 95-104). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_11

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title = "A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm",

abstract = "Many Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study [1] showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the Two-Archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the Two-Archive algorithm outperforms existing MOEAs on problems with a large number of objectives. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

keywords = "Pareto Front, Multiobjective Optimization, Total Size, Strength Pareto Evolutionary Algorithm, Removal Strategy",

author = "Kata PRADITWONG and Xin YAO",

year = "2007",

doi = "10.1007/978-3-540-74377-4_11",

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editor = "Yuping WANG and Yiu-ming CHEUNG and Hailin LIU",

booktitle = "Computational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers",

publisher = "Springer Berlin Heidelberg",

note = "2006 International Conference on Computational and Information Science, CIS 2006 ; Conference date: 03-11-2006 Through 06-11-2006",

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PRADITWONG, K & YAO, X 2007, A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm. in Y WANG, Y CHEUNG & H LIU (eds), Computational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers. Springer Berlin Heidelberg, pp. 95-104, 2006 International Conference on Computational and Information Science, CIS 2006, Guangzhou, China, 3/11/06. https://doi.org/10.1007/978-3-540-74377-4_11

A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm. / PRADITWONG, Kata; YAO, Xin.
Computational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers. ed. / Yuping WANG; Yiu-ming CHEUNG; Hailin LIU. Springer Berlin Heidelberg, 2007. p. 95-104.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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T1 - A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm

AU - PRADITWONG, Kata

AU - YAO, Xin

PY - 2007

Y1 - 2007

N2 - Many Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study [1] showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the Two-Archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the Two-Archive algorithm outperforms existing MOEAs on problems with a large number of objectives. © Springer-Verlag Berlin Heidelberg 2007.

AB - Many Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study [1] showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the Two-Archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the Two-Archive algorithm outperforms existing MOEAs on problems with a large number of objectives. © Springer-Verlag Berlin Heidelberg 2007.

KW - Pareto Front

KW - Multiobjective Optimization

KW - Total Size

KW - Strength Pareto Evolutionary Algorithm

KW - Removal Strategy

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DO - 10.1007/978-3-540-74377-4_11

M3 - Conference paper (refereed)

SN - 9783540743767

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EP - 104

BT - Computational Intelligence and Security : International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers

A2 - WANG, Yuping

A2 - CHEUNG, Yiu-ming

A2 - LIU, Hailin

PB - Springer Berlin Heidelberg

T2 - 2006 International Conference on Computational and Information Science, CIS 2006

Y2 - 3 November 2006 through 6 November 2006

ER -

A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm

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