Improving NSGA-II algorithm based on minimum spanning tree

Miqing Li; Jinhua Zheng; Jun Wu

doi:10.1007/978-3-540-89694-4_18

Improving NSGA-II algorithm based on minimum spanning tree

Miqing Li^*, Jinhua Zheng, Jun Wu

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.

Original language	English
Title of host publication	Simulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings
Pages	170-179
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-540-89694-4_18
Publication status	Published - 2008
Event	7th International Conference on Simulated Evolution and Learning, SEAL 2008 - Melbourne, VIC, Australia Duration: 7 Dec 2008 → 10 Dec 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5361 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Simulated Evolution and Learning, SEAL 2008
Country/Territory	Australia
City	Melbourne, VIC
Period	7/12/08 → 10/12/08

Keywords

Crowding distance
Minimum spanning tree
Multi-objective evolutionary algorithm
Multi-objective optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-89694-4_18

Cite this

Li, M., Zheng, J., & Wu, J. (2008). Improving NSGA-II algorithm based on minimum spanning tree. In Simulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings (pp. 170-179). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5361 LNAI). https://doi.org/10.1007/978-3-540-89694-4_18

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title = "Improving NSGA-II algorithm based on minimum spanning tree",

abstract = "Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.",

keywords = "Crowding distance, Minimum spanning tree, Multi-objective evolutionary algorithm, Multi-objective optimization",

author = "Miqing Li and Jinhua Zheng and Jun Wu",

year = "2008",

doi = "10.1007/978-3-540-89694-4_18",

language = "English",

isbn = "3540896937",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "170--179",

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note = "7th International Conference on Simulated Evolution and Learning, SEAL 2008 ; Conference date: 07-12-2008 Through 10-12-2008",

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Li, M, Zheng, J & Wu, J 2008, Improving NSGA-II algorithm based on minimum spanning tree. in Simulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5361 LNAI, pp. 170-179, 7th International Conference on Simulated Evolution and Learning, SEAL 2008, Melbourne, VIC, Australia, 7/12/08. https://doi.org/10.1007/978-3-540-89694-4_18

Improving NSGA-II algorithm based on minimum spanning tree. / Li, Miqing; Zheng, Jinhua; Wu, Jun.
Simulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings. 2008. p. 170-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5361 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Wu, Jun

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N2 - Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.

AB - Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.

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ER -

Improving NSGA-II algorithm based on minimum spanning tree

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this