A synthesized ranking-assisted NSGA-II for interval multi-objective optimization

Pengfei Zhang; Ruidong Xu; Xiaoyan Sun; Dunwei Gong; Yong Zhang; Jong Choi

doi:10.1109/CEC.2016.7743881

A synthesized ranking-assisted NSGA-II for interval multi-objective optimization

Pengfei Zhang, Ruidong Xu, Xiaoyan Sun, Dunwei Gong, Yong Zhang, Jong Choi

Discrete Algorithms

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

5 Scopus citations

Abstract

Multi-objective optimization problems with interval (MOPs-I) uncertainties parameters are common in practice. Evolutionary multi-objective (EMO) algorithms are popularly employed to solve these problems due to their powerful explorations. The comparison strategies among interval objectives of MOPs-I are crucially important for obtaining a superior Pareto front when applying EMOs. By effectively combining two different intervals ranking methods together, i.e., μ and P metrics, we present an improved NSGA-II with a synthesized intervals ranking strategy for optimizing MOPs-I. The characteristics of μ and P in ranking intervals are first analyzed, and then the synthesized ranking method termed as μ ⊕ P is developed to compare and select individuals within the NSGA-II framework. The proposed algorithm is experimentally validated by four MOPs-I functions and a practical problem, and the results empirically demonstrate its merits in obtaining Pareto front with outstanding convergence and spread.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	861-868
Number of pages	8
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7743881
State	Published - Nov 14 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: Jul 24 2016 → Jul 29 2016

Publication series

Name	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	07/24/16 → 07/29/16

Funding

This work is supported by National Natural Science Foundation of China with Grant No. 61473298 and 61473299.

Keywords

Evolutionary optimization
Interval
Interval ranking
Multi-objective optimization

Access to Document

10.1109/CEC.2016.7743881

Cite this

Zhang, P., Xu, R., Sun, X., Gong, D., Zhang, Y., & Choi, J. (2016). A synthesized ranking-assisted NSGA-II for interval multi-objective optimization. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 861-868). Article 7743881 (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7743881

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title = "A synthesized ranking-assisted NSGA-II for interval multi-objective optimization",

abstract = "Multi-objective optimization problems with interval (MOPs-I) uncertainties parameters are common in practice. Evolutionary multi-objective (EMO) algorithms are popularly employed to solve these problems due to their powerful explorations. The comparison strategies among interval objectives of MOPs-I are crucially important for obtaining a superior Pareto front when applying EMOs. By effectively combining two different intervals ranking methods together, i.e., μ and P metrics, we present an improved NSGA-II with a synthesized intervals ranking strategy for optimizing MOPs-I. The characteristics of μ and P in ranking intervals are first analyzed, and then the synthesized ranking method termed as μ ⊕ P is developed to compare and select individuals within the NSGA-II framework. The proposed algorithm is experimentally validated by four MOPs-I functions and a practical problem, and the results empirically demonstrate its merits in obtaining Pareto front with outstanding convergence and spread.",

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author = "Pengfei Zhang and Ruidong Xu and Xiaoyan Sun and Dunwei Gong and Yong Zhang and Jong Choi",

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Zhang, P, Xu, R, Sun, X, Gong, D, Zhang, Y & Choi, J 2016, A synthesized ranking-assisted NSGA-II for interval multi-objective optimization. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743881, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 861-868, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 07/24/16. https://doi.org/10.1109/CEC.2016.7743881

A synthesized ranking-assisted NSGA-II for interval multi-objective optimization. / Zhang, Pengfei; Xu, Ruidong; Sun, Xiaoyan et al.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 861-868 7743881 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

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

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AU - Xu, Ruidong

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AU - Gong, Dunwei

AU - Zhang, Yong

AU - Choi, Jong

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AB - Multi-objective optimization problems with interval (MOPs-I) uncertainties parameters are common in practice. Evolutionary multi-objective (EMO) algorithms are popularly employed to solve these problems due to their powerful explorations. The comparison strategies among interval objectives of MOPs-I are crucially important for obtaining a superior Pareto front when applying EMOs. By effectively combining two different intervals ranking methods together, i.e., μ and P metrics, we present an improved NSGA-II with a synthesized intervals ranking strategy for optimizing MOPs-I. The characteristics of μ and P in ranking intervals are first analyzed, and then the synthesized ranking method termed as μ ⊕ P is developed to compare and select individuals within the NSGA-II framework. The proposed algorithm is experimentally validated by four MOPs-I functions and a practical problem, and the results empirically demonstrate its merits in obtaining Pareto front with outstanding convergence and spread.

KW - Evolutionary optimization

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A synthesized ranking-assisted NSGA-II for interval multi-objective optimization

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