A comparative study use of OTL for many-objective optimization

Jinhua Zheng; Hui Bai; Ruimin Shen; Miqing Li

doi:10.1145/2739482.2764707

A comparative study use of OTL for many-objective optimization

Jinhua Zheng, Hui Bai, Ruimin Shen, Miqing Li

Computer Science

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

1 Citation (Scopus)

Abstract

This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.

Original language	English
Title of host publication	GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
Editors	Sara Silva
Publisher	Association for Computing Machinery
Pages	1411-1412
Number of pages	2
ISBN (Electronic)	9781450334884
DOIs	https://doi.org/10.1145/2739482.2764707
Publication status	Published - 11 Jul 2015
Event	17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015

Publication series

Name	GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Conference

Conference	17th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/Territory	Spain
City	Madrid
Period	11/07/15 → 15/07/15

Bibliographical note

Funding Information:
The authors wish to thank the support of the National Natural Science Foundation of China (Grant Nos. 61379062, 61372049, 61403326), the Science Research Project of the Education Office of Hunan Province (Grant Nos. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant Nos. 14JJ2072, 13JJ8006), the Science and Technology Project of Hunan Province (Grant No. 2014GK3027), the Construct Program of the Key Discipline in Hunan Province, and the Hunan Provincial Innovation Foundation For Post- graduate (Grant No. CX2013A011).

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence

Access to Document

10.1145/2739482.2764707

Cite this

Zheng, J., Bai, H., Shen, R., & Li, M. (2015). A comparative study use of OTL for many-objective optimization. In S. Silva (Ed.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 1411-1412). (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference). Association for Computing Machinery . https://doi.org/10.1145/2739482.2764707

Zheng, Jinhua ; Bai, Hui ; Shen, Ruimin et al. / A comparative study use of OTL for many-objective optimization. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. editor / Sara Silva. Association for Computing Machinery , 2015. pp. 1411-1412 (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference).

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title = "A comparative study use of OTL for many-objective optimization",

abstract = "This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.",

author = "Jinhua Zheng and Hui Bai and Ruimin Shen and Miqing Li",

note = "Funding Information: The authors wish to thank the support of the National Natural Science Foundation of China (Grant Nos. 61379062, 61372049, 61403326), the Science Research Project of the Education Office of Hunan Province (Grant Nos. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant Nos. 14JJ2072, 13JJ8006), the Science and Technology Project of Hunan Province (Grant No. 2014GK3027), the Construct Program of the Key Discipline in Hunan Province, and the Hunan Provincial Innovation Foundation For Post- graduate (Grant No. CX2013A011).; 17th Genetic and Evolutionary Computation Conference, GECCO 2015 ; Conference date: 11-07-2015 Through 15-07-2015",

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Zheng, J, Bai, H, Shen, R & Li, M 2015, A comparative study use of OTL for many-objective optimization. in S Silva (ed.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, Association for Computing Machinery , pp. 1411-1412, 17th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11/07/15. https://doi.org/10.1145/2739482.2764707

A comparative study use of OTL for many-objective optimization. / Zheng, Jinhua; Bai, Hui; Shen, Ruimin et al.
GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. ed. / Sara Silva. Association for Computing Machinery , 2015. p. 1411-1412 (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference).

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

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AU - Zheng, Jinhua

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N1 - Funding Information: The authors wish to thank the support of the National Natural Science Foundation of China (Grant Nos. 61379062, 61372049, 61403326), the Science Research Project of the Education Office of Hunan Province (Grant Nos. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant Nos. 14JJ2072, 13JJ8006), the Science and Technology Project of Hunan Province (Grant No. 2014GK3027), the Construct Program of the Key Discipline in Hunan Province, and the Hunan Provincial Innovation Foundation For Post- graduate (Grant No. CX2013A011).

PY - 2015/7/11

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N2 - This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.

AB - This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.

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Zheng J, Bai H, Shen R, Li M. A comparative study use of OTL for many-objective optimization. In Silva S, editor, GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery . 2015. p. 1411-1412. (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference). doi: 10.1145/2739482.2764707

A comparative study use of OTL for many-objective optimization

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this